├── requirements.txt ├── gdown_folder.py ├── README.md ├── get_boxes.py ├── LICENSE ├── CVPR25_text_eval.py ├── SurfaceDice.py └── CVPR25_iter_eval.py /requirements.txt: -------------------------------------------------------------------------------- 1 | torch 2 | connected-components-3d==3.12.4 3 | pandas==2.2.1 4 | numpy==1.26.3 5 | scipy==1.12.0 6 | cupy-cuda12x 7 | cucim==23.10.0 8 | tqdm 9 | scikit-image 10 | -------------------------------------------------------------------------------- /gdown_folder.py: -------------------------------------------------------------------------------- 1 | import os 2 | import re 3 | import sys 4 | import time 5 | import requests 6 | import contextlib 7 | from concurrent.futures import ThreadPoolExecutor, as_completed 8 | from tqdm import tqdm # Progress bar for downloads 9 | 10 | def recursive_gdown(folder_id, current_path='', max_workers=4, quiet_gdown=False): 11 | url = f"https://drive.google.com/embeddedfolderview?id={folder_id}" 12 | response = requests.get(url) 13 | if response.status_code != 200: 14 | print(f"Error {response.status_code}: {response.content}") 15 | return 16 | 17 | data = response.text 18 | 19 | # Extracting .npz filenames from the HTML 20 | npz_pattern = r'
([^<]*?\.npz)
' 21 | npz_files = re.findall(npz_pattern, data) 22 | 23 | folder_title_match = re.search(r"(.*?)", data) 24 | folder_title = folder_title_match.group(1) if folder_title_match else "Unknown" 25 | 26 | # Optimized regex patterns to find file links and subfolders 27 | file_pattern = r"https://drive\.google\.com/file/d/([-\w]{25,})/view" 28 | folder_pattern = r"https://drive.google.com/drive/folders/([-\w]{25,})" 29 | 30 | files = re.findall(file_pattern, data) 31 | folders = re.findall(folder_pattern, data) 32 | if len(files) > 0: 33 | print(f"Found {len(files)} files and {len(folders)} folders in '{folder_title}'") 34 | print(f"Found {len(npz_files)} .npz files") 35 | 36 | # Create directory for current folder 37 | path = os.path.join(current_path, folder_title) 38 | os.makedirs(path, exist_ok=True) 39 | 40 | # Multi-threaded file downloads for .npz files 41 | def download_file(npz_filename): 42 | file_path = os.path.join(path, npz_filename) 43 | 44 | # Check if the file already exists 45 | if os.path.exists(file_path): 46 | print(f"File '{npz_filename}' already exists, skipping...") 47 | return npz_filename # Skip downloading 48 | 49 | # Construct the gdown download command 50 | file_url = f"https://drive.google.com/uc?id={files[npz_files.index(npz_filename)]}" 51 | 52 | output_redirect = " > nul 2>&1" if os.name == "nt" else " > /dev/null 2>&1" if quiet_gdown else "" 53 | command = f"gdown {file_url} -O \"{file_path}\"{output_redirect}" 54 | 55 | exit_code = os.system(command) 56 | 57 | num_tries = 0 58 | while exit_code != 0: 59 | if num_tries > 3: 60 | print(f'Tried downloading {npz_filename} already {num_tries} times unsuccessfully. Please re-download your cookies.txt and put them in ~/.cache/gdown/') 61 | exit(1) 62 | #print(f"Retrying {npz_filename} in 30 seconds...") 63 | time.sleep(30) 64 | exit_code = os.system(command) # Retry downloading 65 | num_tries += 1 66 | 67 | return npz_filename # Return filename to update progress bar 68 | 69 | # Progress bar setup 70 | progress_bar = tqdm(total=len(npz_files), desc="Downloading .npz Files", unit="file") 71 | 72 | with ThreadPoolExecutor(max_workers=max_workers) as executor: 73 | future_to_file = {executor.submit(download_file, npz_filename): npz_filename for npz_filename in npz_files} 74 | for future in as_completed(future_to_file): 75 | future.result() # Wait for each file download to complete 76 | progress_bar.update(1) 77 | 78 | progress_bar.close() 79 | 80 | # Recursively process each sub-folder 81 | for folder_id in folders: 82 | recursive_gdown(folder_id, path, max_workers, quiet_gdown) 83 | 84 | if __name__ == "__main__": 85 | if len(sys.argv) < 2: 86 | print(f"Usage: python {sys.argv[0]} [max_workers] [--quiet-gdown]") 87 | exit(1) 88 | 89 | folder_id = sys.argv[1] 90 | if not re.match(r"^[-\w]{25,}$", folder_id): 91 | print(f"Invalid ID: {folder_id}") 92 | exit(1) 93 | 94 | # Get max_workers from user input or default to 4 95 | max_workers = 4 96 | quiet_gdown = False 97 | 98 | if len(sys.argv) > 2: 99 | for arg in sys.argv[2:]: 100 | if arg.isdigit(): 101 | max_workers = int(arg) 102 | elif arg == "--quiet-gdown": 103 | quiet_gdown = True 104 | 105 | if max_workers == 0:# Use all cores 106 | import multiprocessing # Detect CPU cores 107 | max_workers = multiprocessing.cpu_count() 108 | 109 | recursive_gdown(folder_id, "./SegFM3D", max_workers, quiet_gdown) 110 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # CVPR-MedSegFMCompetition 2 | Foundation Models for Biomedical Image Segmentation 3 | 4 | ## Evaluation 5 | The evaluation script `CVPR25_iter_eval.py` evaluates Docker submissions for the **CVPR25: Foundation Models for Interactive 3D Biomedical Image Segmentation Challenge** using an iterative refinement approach. 6 | 7 | ### Installation 8 | Installation of packages for the evaluation script: 9 | ``` 10 | conda create -n cvpr_segfm_eval python=3.11 -y 11 | conda activate cvpr_segfm_eval 12 | pip install -r requirements.txt 13 | ``` 14 | 15 | Run the script as follows: 16 | 17 | ```bash 18 | python CVPR25_iter_eval.py --docker_folder path/to/docker_submissions --test_img_path path/to/test_images --save_path path/to/output --verbose 19 | ``` 20 | 21 | ### Arguments 22 | - `--docker_folder` : Path to the directory containing submitted Docker containers (`.tar.gz`). 23 | - `--test_img_path` : Path to the directory containing `.npz` test images. 24 | - `--save_path` : Directory to save segmentation outputs and evaluation metrics. 25 | - `--verbose` *(optional)* : Enables detailed output, including generated click coordinates. 26 | - `--validation_gts_path` Path to validation / test set GT files. This is needed to prevent label leakage (val/test) during the challenge. 27 | 28 | ### Evaluation Process 29 | 1. **Loads Docker submissions** and processes test images one by one. 30 | 2. **Initial Prediction:** Uses a bounding box prompt to generate the first segmentation. 31 | 3. **Iterative Refinement:** Simulates up to 5 refinement clicks based on segmentation errors. 32 | 4. **Performance Metrics:** Computes **Dice Similarity Coefficient AUC (DSC_AUC), Normalized Surface Dice AUC (NSD_AUC), Final DSC, Final NSD, and Inference Time**. 33 | 5. **Outputs results** as `.npz` files and a CSV summary. 34 | 35 | ### Output 36 | - Segmentation results are saved in the specified output directory. 37 | - Final prediction in the `segs` key 38 | - All the 6 intermediate predictions in the `all_segs` key 39 | - Metrics for each test case are compiled into a CSV file. 40 | 41 | For more details, refer to the challenge page: https://www.codabench.org/competitions/5263/ 42 | 43 | 44 | ### Clicks Accumulation in Image Input 45 | 46 | During the prediction process, clicks are accumulated in the `clicks` key within the input `.npz` file. 47 | 48 | An example of a list stored in the `clicks` key for an image with 4 targets and after all 5 clicks: 49 | 50 | ```json 51 | [ 52 | {"fg": [[46, 336, 343], [28, 233, 365], [28, 233, 365], [28, 233, 365]], "bg": [[28, 233, 366]]}, 53 | {"fg": [[38, 210, 148]], "bg": [[6, 230, 284], [6, 230, 284], [6, 230, 284], [6, 230, 284]]}, 54 | {"fg": [[12, 287, 262], [12, 287, 262], [12, 287, 262], [12, 287, 262], [12, 287, 262]], "bg": []}, 55 | {"fg": [[28, 199, 180], [28, 199, 180], [28, 199, 180], [28, 199, 180], [28, 199, 180]], "bg": []}, 56 | ] 57 | ``` 58 | ### Clicks Order 59 | We also provide the order in which the clicks were generated in a ancilliary key `clicks_order` that is a simple list with values `fg` and `bg`, e.g., `['fg', 'fg', 'bg']`, indicating that the first two clicks were foreground clicks and the last a background click. 60 | ### Previous Prediction in Image Input 61 | 62 | The input image also contains the `prev_pred` key which stores the prediction from the previous iteration. This is used only to help with submissions that are using the previous prediction as an additional input. 63 | 64 | ### No Bounding Box key 65 | We also omit the `boxes` key in some of the validation and test samples as it is a bad prompt for some structures, such as vessels. In this case we simply skip the first inital prediction and only evaluate the models with 5 clicks using the same evaluation metrics. 66 | 67 | 68 | ### Upper Time Bound During Testing 69 | We set a limit of 90 seconds per class during inference (whole docker run). If the inference time exceeds this bound, the corresponding DSC and NSD scores will be set as 0. When participants evaluate their models using the `CVPR25_iter_eval.py` script they will receive a warning if their models exceed this limit. 70 | 71 | There are two motivations for this setting 72 | - The main focus of this competition is to prompt the interactive segmentation algorithm designs. Inference time should not be a huge concern/constraint for participants. 73 | - It is very hard to evaluate the real inference time within docker since implementations also affect the docker overhead. 74 | 75 | ### Final Script Output 76 | The `CVPR25_iter_eval.py` script will produce the following outputs in the `--save_path` argument: 77 | - `{teamname}_metrics.csv` that contains the following columns 78 | - `CaseName`: Test / Validation image filename 79 | - `TotalRunningTime`: Inference time taken for the image (all interactions) 80 | - `RunningTime_{i}`: Inference time for interactions [1-6], 1: bbox, 2-6: clicks 81 | - `DSC_AUC`: Area under DSC-to-Click curve metric 82 | - `NSD_AUC`: Area under NSD-to-Click curve metric 83 | - `DSC_Final`: DSC after final click 84 | - `NSD_Final`: NSD after final click 85 | - `CASE_{i}.npz` - model output with keys: 86 | - `segs`: Final prediction for all classes 87 | - `all_segs`: All intermediate predictions of the model for interactions [1-6] 88 | 89 | -------------------------------------------------------------------------------- /get_boxes.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | import cv2 4 | np.random.seed(2025) 5 | import cc3d 6 | from skimage import segmentation 7 | import copy 8 | import multiprocessing as mp 9 | import glob 10 | 11 | def show_box_cv2(image, box, color=(255, 0, 0), thickness=2): 12 | """ 13 | Draws a rectangle on an image using OpenCV. 14 | Args: 15 | image: The input image (numpy array). 16 | box: A bounding box, either 2D ([x_min, y_min, x_max, y_max]) or 3D ([x_min, y_min, z_min, x_max, y_max, z_max]). 17 | color: Color of the rectangle in BGR (default is blue). 18 | thickness: Thickness of the rectangle border (default is 2). 19 | Returns: 20 | The image with the rectangle drawn. 21 | """ 22 | color = tuple(map(int, color)) 23 | if len(box) == 4: # 2D bounding box 24 | x_min, y_min, x_max, y_max = box 25 | cv2.rectangle(image, (x_min, y_min), (x_max, y_max), color, thickness) 26 | else: # 3D bounding box 27 | x_min, y_min, z_min, x_max, y_max, z_max = box 28 | cv2.rectangle(image, (x_min, y_min), (x_max, y_max), color, thickness) 29 | return image 30 | 31 | def show_mask_cv2(mask, image, color=None, alpha=0.5): 32 | assert mask.sum()>0 33 | if color is None: 34 | color = np.random.randint(0, 255, 3) 35 | h, w = mask.shape[-2:] 36 | overlay = np.zeros_like(image) 37 | for i in range(3): 38 | overlay[:, :, i] = color[i] 39 | overlay = cv2.bitwise_and(overlay, overlay, mask=mask) 40 | combined = cv2.addWeighted(overlay, alpha, image, 1-alpha , 0) 41 | 42 | return combined 43 | 44 | def mask2D_to_bbox(gt2D, file): 45 | try: 46 | y_indices, x_indices = np.where(gt2D > 0) 47 | x_min, x_max = np.min(x_indices), np.max(x_indices) 48 | y_min, y_max = np.min(y_indices), np.max(y_indices) 49 | # add perturbation to bounding box coordinates 50 | H, W = gt2D.shape 51 | bbox_shift = np.random.randint(0, 6, 1)[0] 52 | scale_y, scale_x = gt2D.shape 53 | bbox_shift_x = int(bbox_shift * scale_x/256) 54 | bbox_shift_y = int(bbox_shift * scale_y/256) 55 | #print(f'{bbox_shift_x=} {bbox_shift_y=} with orig {bbox_shift=}') 56 | x_min = max(0, x_min - bbox_shift_x) 57 | x_max = min(W-1, x_max + bbox_shift_x) 58 | y_min = max(0, y_min - bbox_shift_y) 59 | y_max = min(H-1, y_max + bbox_shift_y) 60 | boxes = np.array([x_min, y_min, x_max, y_max]) 61 | return boxes 62 | except Exception as e: 63 | raise Exception(f'error {e} with file {file}') 64 | 65 | 66 | def mask3D_to_bbox(gt3D, file): 67 | b_dict = {} 68 | z_indices, y_indices, x_indices = np.where(gt3D > 0) 69 | z_min, z_max = np.min(z_indices), np.max(z_indices) 70 | z_indices = np.unique(z_indices) 71 | # middle of z_indices 72 | z_middle = z_indices[len(z_indices)//2] 73 | 74 | D, H, W = gt3D.shape 75 | b_dict['z_min'] = z_min 76 | b_dict['z_max'] = z_max 77 | b_dict['z_mid'] = z_middle 78 | 79 | gt_mid = gt3D[z_middle] 80 | 81 | box_2d = mask2D_to_bbox(gt_mid, file) 82 | x_min, y_min, x_max, y_max = box_2d 83 | b_dict['z_mid_x_min'] = x_min 84 | b_dict['z_mid_y_min'] = y_min 85 | b_dict['z_mid_x_max'] = x_max 86 | b_dict['z_mid_y_max'] = y_max 87 | 88 | assert z_min == max(0, z_min) 89 | assert z_max == min(D-1, z_max) 90 | return b_dict 91 | 92 | path = 'path-to-npz-files' 93 | path_dest = 'destination-path' 94 | os.makedirs(path_dest, exist_ok=True) 95 | sanity_dir = os.path.join(path_dest, 'sanity') 96 | os.makedirs(sanity_dir, exist_ok=True) 97 | files = glob.glob(os.path.join(path, '*/*/*.npz')) 98 | files = [x for x in files if 'Microscopy' not in x] 99 | files = sorted(files) 100 | 101 | print(f'number of files {len(files)}') 102 | 103 | def process(file): 104 | print(f'processing file {file}') 105 | 106 | npz = np.load(file, allow_pickle=True) 107 | imgs = npz['imgs'] 108 | 109 | gts = npz['gts'] 110 | gts, _, _ = segmentation.relabel_sequential(gts) 111 | spacing = npz['spacing'] 112 | unique_labs = np.unique(gts)[1:] 113 | 114 | boxes_list = [] 115 | for lab in unique_labs: 116 | gt = gts==lab 117 | box_dict = mask3D_to_bbox(gt, file) 118 | boxes_list.append(box_dict) 119 | 120 | for j, box_dict in enumerate(boxes_list): 121 | color = np.random.randint(0, 255, 3) 122 | img_mid = imgs[box_dict['z_mid']].copy() 123 | img_mid = np.expand_dims(img_mid, axis=-1).repeat(3, axis=-1) 124 | box2D = [box_dict['z_mid_x_min'], box_dict['z_mid_y_min'], box_dict['z_mid_x_max'], box_dict['z_mid_y_max']] 125 | img_mid = show_box_cv2(img_mid, box2D, color=color, thickness=2) 126 | img_mid = show_mask_cv2((gts[box_dict['z_mid']]==unique_labs[j]).astype(np.uint8), img_mid.astype(np.uint8), color=color, alpha=0.5) 127 | cv2.imwrite(os.path.join(sanity_dir, os.path.basename(file).replace('.npz', f'_boxIdx{j}.png')), img_mid) 128 | 129 | assert gt.sum() > 0 130 | np.savez_compressed(os.path.join(path_dest, os.path.basename(file)), imgs=imgs, gts=gts, boxes=boxes_list, spacing=spacing) 131 | 132 | if __name__ == '__main__': 133 | with mp.Pool(16) as p: 134 | p.map(process, files) 135 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /CVPR25_text_eval.py: -------------------------------------------------------------------------------- 1 | """ 2 | The code was adapted from the MICCAI FLARE Challenge 3 | https://github.com/JunMa11/FLARE 4 | 5 | The testing images will be evaluated one by one. 6 | 7 | Folder structure: 8 | CVPR25_text_eval.py 9 | - team_docker 10 | - teamname.tar.gz # submitted docker containers from participants 11 | - test_demo 12 | - imgs 13 | - case1.npz # testing image 14 | - case2.npz 15 | - ... 16 | - demo_seg # segmentation results *******segmentation key: ['segs']******* 17 | - case1.npz # segmentation file name is the same as the testing image name 18 | - case2.npz 19 | - ... 20 | """ 21 | 22 | import os 23 | join = os.path.join 24 | import shutil 25 | import time 26 | import torch 27 | import argparse 28 | from collections import OrderedDict 29 | import pandas as pd 30 | import numpy as np 31 | from skimage import segmentation 32 | from scipy.optimize import linear_sum_assignment 33 | import cc3d 34 | import SimpleITK as sitk 35 | 36 | from SurfaceDice import compute_surface_distances, compute_surface_dice_at_tolerance, compute_dice_coefficient 37 | 38 | def compute_multi_class_dsc(gt, seg, label_ids): 39 | present_labels = set(np.unique(gt)[1:]) & set(label_ids) 40 | dsc = [None] * len(present_labels) 41 | for idx, i in enumerate(present_labels): 42 | gt_i = gt == i 43 | seg_i = seg == i 44 | dsc[idx] = compute_dice_coefficient(gt_i, seg_i) 45 | return np.nanmean(dsc) 46 | 47 | def compute_multi_class_nsd(gt, seg, spacing, label_ids, tolerance=2.0): 48 | present_labels = set(np.unique(gt)[1:]) & set(label_ids) 49 | nsd = [None] * len(present_labels) 50 | for idx, i in enumerate(present_labels): 51 | gt_i = gt == i 52 | seg_i = seg == i 53 | surface_distance = compute_surface_distances(gt_i, seg_i, spacing_mm=spacing) 54 | nsd[idx] = compute_surface_dice_at_tolerance(surface_distance, tolerance) 55 | return np.nanmean(nsd) 56 | 57 | def _label_overlap(x, y): 58 | """ fast function to get pixel overlaps between masks in x and y 59 | 60 | Parameters 61 | ------------ 62 | 63 | x: ND-array, int 64 | where 0=NO masks; 1,2... are mask labels 65 | y: ND-array, int 66 | where 0=NO masks; 1,2... are mask labels 67 | 68 | Returns 69 | ------------ 70 | 71 | overlap: ND-array, int 72 | matrix of pixel overlaps of size [x.max()+1, y.max()+1] 73 | 74 | """ 75 | x = x.ravel() 76 | y = y.ravel() 77 | 78 | # preallocate a 'contact map' matrix 79 | overlap = np.zeros((1+x.max(),1+y.max()), dtype=np.uint) 80 | 81 | # loop over the labels in x and add to the corresponding 82 | # overlap entry. If label A in x and label B in y share P 83 | # pixels, then the resulting overlap is P 84 | # len(x)=len(y), the number of pixels in the whole image 85 | for i in range(len(x)): 86 | overlap[x[i],y[i]] += 1 87 | return overlap 88 | 89 | def _intersection_over_union(masks_true, masks_pred): 90 | """ intersection over union of all mask pairs 91 | 92 | Parameters 93 | ------------ 94 | 95 | masks_true: ND-array, int 96 | ground truth masks, where 0=NO masks; 1,2... are mask labels 97 | masks_pred: ND-array, int 98 | predicted masks, where 0=NO masks; 1,2... are mask labels 99 | 100 | Returns 101 | ------------ 102 | iou: ND-array, float 103 | matrix of IOU pairs of size [masks_true.max()+1, masks_pred.max()+1] 104 | iou[i, j] is the IoU between ground truth instance i+1 and predicted instance j+1. 105 | """ 106 | overlap = _label_overlap(masks_true, masks_pred) 107 | n_pixels_pred = np.sum(overlap, axis=0, keepdims=True) 108 | n_pixels_true = np.sum(overlap, axis=1, keepdims=True) 109 | iou = overlap / (n_pixels_pred + n_pixels_true - overlap) 110 | iou[np.isnan(iou)] = 0.0 111 | return iou 112 | 113 | def _true_positive(iou, th): 114 | """ true positive at threshold th 115 | 116 | Parameters 117 | ------------ 118 | 119 | iou: float, ND-array 120 | array of IOU pairs 121 | th: float 122 | threshold on IOU for positive label 123 | 124 | Returns 125 | ------------ 126 | 127 | tp: float 128 | number of true positives at threshold 129 | """ 130 | n_min = min(iou.shape[0], iou.shape[1]) 131 | costs = -(iou >= th).astype(float) - iou / (2*n_min) 132 | true_ind, pred_ind = linear_sum_assignment(costs) 133 | match_ok = iou[true_ind, pred_ind] >= th 134 | tp = match_ok.sum() 135 | matched_pairs = [(t, p) for t, p, ok in zip(true_ind, pred_ind, match_ok) if ok] 136 | return tp, matched_pairs 137 | 138 | def eval_tp_fp_fn(masks_true, masks_pred, threshold=0.5): 139 | num_inst_gt = np.max(masks_true) 140 | num_inst_seg = np.max(masks_pred) 141 | if num_inst_seg>0: 142 | iou = _intersection_over_union(masks_true, masks_pred)[1:, 1:] 143 | tp, matched_pairs = _true_positive(iou, threshold) 144 | fp = num_inst_seg - tp 145 | fn = num_inst_gt - tp 146 | else: 147 | # print('No segmentation results!') 148 | tp = 0 149 | fp = 0 150 | fn = 0 151 | matched_pairs = None 152 | 153 | return tp, fp, fn, matched_pairs 154 | 155 | parser = argparse.ArgumentParser('Segmentation eavluation for docker containers', add_help=False) 156 | parser.add_argument('-i', '--test_img_path', default='./3D_val_npz', type=str, help='testing data path') 157 | parser.add_argument('-val_gts','--validation_gts_path', default='./3D_val_gt_text_seg', type=str, help='path to validation set (or final test set) GT files') 158 | parser.add_argument('-o','--save_path', default='./outputs', type=str, help='segmentation output path') 159 | parser.add_argument('-d','--docker_folder_path', default='./team_dockers', type=str, help='team docker path') 160 | args = parser.parse_args() 161 | 162 | test_img_path = args.test_img_path 163 | validation_gts_path = args.validation_gts_path 164 | save_path = args.save_path 165 | docker_path = args.docker_folder_path 166 | 167 | input_temp = './inputs/' 168 | output_temp = './outputs' 169 | os.makedirs(save_path, exist_ok=True) 170 | 171 | dockers = sorted(os.listdir(docker_path)) 172 | test_cases = sorted(os.listdir(test_img_path)) 173 | 174 | for docker in dockers: 175 | try: 176 | # create temp folers for inference one-by-one 177 | if os.path.exists(input_temp): 178 | shutil.rmtree(input_temp) 179 | if os.path.exists(output_temp): 180 | shutil.rmtree(output_temp) 181 | os.makedirs(input_temp) 182 | os.makedirs(output_temp) 183 | 184 | # load docker and create a new folder to save segmentation results 185 | teamname = docker.split('.')[0].lower() 186 | print('teamname docker: ', docker) 187 | os.system('docker image load -i {}'.format(join(docker_path, docker))) 188 | 189 | # create a new folder to save segmentation results 190 | team_outpath = join(save_path, teamname) 191 | if os.path.exists(team_outpath): 192 | shutil.rmtree(team_outpath) 193 | os.mkdir(team_outpath) 194 | os.system('chmod -R 777 ./* ') # give permission to all files 195 | 196 | # initialize the metric dictionary 197 | metric = OrderedDict() 198 | metric['CaseName'] = [] 199 | metric['RunningTime'] = [] 200 | metric['DSC'] = [] 201 | metric['NSD'] = [] 202 | metric['F1'] = [] 203 | metric['DSC_TP'] = [] 204 | 205 | missing_files = [] 206 | 207 | # To obtain the running time for each case, testing cases are inferred one-by-one 208 | for case in test_cases: 209 | shutil.copy(join(test_img_path, case), input_temp) 210 | cmd = 'docker container run --gpus "device=0" -m 32G --name {} --rm -v $PWD/inputs/:/workspace/inputs/ -v $PWD/outputs/:/workspace/outputs/ {}:latest /bin/bash -c "sh predict.sh" '.format(teamname, teamname) 211 | print(teamname, ' docker command:', cmd, '\n', 'testing image name:', case) 212 | 213 | # run the docker container and measure inference time 214 | start_time = time.time() 215 | try: 216 | os.system(cmd) 217 | except Exception as e: 218 | print('inference error!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!') 219 | print(case, e) 220 | real_running_time = time.time() - start_time 221 | print(f"{case} finished! Inference time: {real_running_time}") 222 | 223 | # save metrics 224 | metric['CaseName'].append(case) 225 | metric['RunningTime'].append(real_running_time) 226 | 227 | # Metric calculation (DSC and NSD) 228 | seg_name = case 229 | gt_path = join(validation_gts_path, seg_name) 230 | seg_path = join(output_temp, seg_name) 231 | 232 | try: 233 | # Load ground truth and segmentation masks 234 | gt_npz = np.load(gt_path, allow_pickle=True)['gts'] 235 | seg_npz = np.load(seg_path, allow_pickle=True)['segs'] 236 | 237 | gt_npz = gt_npz.astype(np.uint8) 238 | seg_npz = seg_npz.astype(np.uint8) 239 | 240 | # Calculate DSC and NSD 241 | img_npz = np.load(join(input_temp, case), allow_pickle=True) 242 | spacing = img_npz['spacing'] 243 | instance_label = img_npz['text_prompts'].item()['instance_label'] 244 | 245 | class_ids = sorted([int(k) for k in img_npz['text_prompts'].item() if k != "instance_label"]) 246 | class_ids_array = np.array(class_ids, dtype=np.int32) 247 | 248 | if instance_label == 0: # semantic masks 249 | # note: the semantic labels may not be sequential 250 | dsc = compute_multi_class_dsc(gt_npz, seg_npz, class_ids_array) 251 | nsd = compute_multi_class_nsd(gt_npz, seg_npz, spacing, class_ids_array) 252 | f1_score = np.NaN 253 | dsc_tp = np.NaN 254 | elif instance_label == 1: # instance masks 255 | # Calculate F1 instead 256 | if len(np.unique(seg_npz)) == 2: 257 | print("converting segmentation to instance masks") 258 | # convert prediction masks from binary to instance 259 | tumor_inst, tumor_n = cc3d.connected_components(seg_npz, connectivity=6, return_N=True) 260 | 261 | # put the tumor instances back to gt_data_ori 262 | seg_npz[tumor_inst > 0] = (tumor_inst[tumor_inst > 0] + np.max(seg_npz)) 263 | 264 | gt_npz = segmentation.relabel_sequential(gt_npz)[0] 265 | seg_npz = segmentation.relabel_sequential(seg_npz)[0] 266 | 267 | tp, fp, fn, matched_pairs = eval_tp_fp_fn(gt_npz, seg_npz) # default f1 overlap threshold is 0.5 268 | precision = tp / (tp + fp) if (tp + fp) > 0 else 0 269 | recall = tp / (tp + fn) if (tp + fn) > 0 else 0 270 | f1_score = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0 271 | 272 | # compute DSC for TP cases 273 | if matched_pairs: 274 | dsc_list = [] 275 | for gt_idx, pred_idx in matched_pairs: 276 | gt_mask = gt_npz == (gt_idx + 1) 277 | pred_mask = seg_npz == (pred_idx + 1) 278 | dsc_value = compute_dice_coefficient(gt_mask, pred_mask) 279 | dsc_list.append(dsc_value) 280 | dsc_tp = np.mean(dsc_list) 281 | else: 282 | dsc_tp = 0 283 | 284 | # Set DSC and NSD to None for instance masks 285 | dsc = None 286 | nsd = None 287 | 288 | metric['DSC'].append(round(dsc, 4) if dsc is not None else np.NAN) 289 | metric['NSD'].append(round(nsd, 4) if nsd is not None else np.NAN) 290 | metric['F1'].append(round(f1_score, 4) if f1_score is not None else np.NAN) 291 | metric['DSC_TP'].append(round(dsc_tp, 4) if dsc_tp is not None else np.NAN) 292 | 293 | print(f"{case}: DSC={dsc if dsc is not None else np.NAN}, NSD={nsd if nsd is not None else np.NAN}, F1={f1_score}, DSC_TP={dsc_tp if dsc_tp is not None else np.NAN}") 294 | 295 | except Exception as e: 296 | print(f"ERROR processing {case}: {e}") 297 | metric['DSC'].append(np.NAN) 298 | metric['NSD'].append(np.NAN) 299 | metric['F1'].append(np.NAN) 300 | missing_files.append(f"{case}: {e}") 301 | 302 | # the segmentation file name should be the same as the testing image name 303 | try: 304 | os.rename(join(output_temp, seg_name), join(team_outpath, seg_name)) 305 | except: 306 | print(f"{join(output_temp, seg_name)}, {join(team_outpath, seg_name)}") 307 | print("Wrong segmentation name!!! It should be the same as image_name") 308 | 309 | os.remove(join(input_temp, case)) # Moves the segmentation output file from output_temp to the appropriate team folder in demo_seg. 310 | 311 | # save the metrics to a CSV file 312 | metric_df = pd.DataFrame(metric) 313 | metric_df.to_csv(join(team_outpath, teamname + '_metrics.csv'), index=False) 314 | print(f"Metrics saved to {join(team_outpath, teamname + '_metrics.csv')}") 315 | 316 | # Save missing files log 317 | if missing_files: 318 | missing_file_path = os.path.join(team_outpath, f"{teamname}_error_files.txt") 319 | with open(missing_file_path, 'w') as f: 320 | f.write("\n".join(missing_files)) 321 | print(f"Error files logged to {missing_file_path}") 322 | 323 | # clean up 324 | torch.cuda.empty_cache() 325 | os.system("docker rmi {}:latest".format(teamname)) 326 | shutil.rmtree(input_temp) 327 | shutil.rmtree(output_temp) 328 | 329 | except Exception as e: 330 | print(e) 331 | -------------------------------------------------------------------------------- /SurfaceDice.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Fri Apr 15 13:01:08 2022 4 | 5 | @author: 12593 6 | """ 7 | 8 | import numpy as np 9 | import scipy.ndimage 10 | 11 | # neighbour_code_to_normals is a lookup table. 12 | # For every binary neighbour code 13 | # (2x2x2 neighbourhood = 8 neighbours = 8 bits = 256 codes) 14 | # it contains the surface normals of the triangles (called "surfel" for 15 | # "surface element" in the following). The length of the normal 16 | # vector encodes the surfel area. 17 | # 18 | # created by compute_surface_area_lookup_table.ipynb using the 19 | # marching_cube algorithm, see e.g. https://en.wikipedia.org/wiki/Marching_cubes 20 | # credit to: http://medicaldecathlon.com/files/Surface_distance_based_measures.ipynb 21 | neighbour_code_to_normals = [ 22 | [[0,0,0]], 23 | [[0.125,0.125,0.125]], 24 | [[-0.125,-0.125,0.125]], 25 | [[-0.25,-0.25,0.0],[0.25,0.25,-0.0]], 26 | [[0.125,-0.125,0.125]], 27 | [[-0.25,-0.0,-0.25],[0.25,0.0,0.25]], 28 | [[0.125,-0.125,0.125],[-0.125,-0.125,0.125]], 29 | [[0.5,0.0,-0.0],[0.25,0.25,0.25],[0.125,0.125,0.125]], 30 | [[-0.125,0.125,0.125]], 31 | [[0.125,0.125,0.125],[-0.125,0.125,0.125]], 32 | [[-0.25,0.0,0.25],[-0.25,0.0,0.25]], 33 | [[0.5,0.0,0.0],[-0.25,-0.25,0.25],[-0.125,-0.125,0.125]], 34 | [[0.25,-0.25,0.0],[0.25,-0.25,0.0]], 35 | [[0.5,0.0,0.0],[0.25,-0.25,0.25],[-0.125,0.125,-0.125]], 36 | [[-0.5,0.0,0.0],[-0.25,0.25,0.25],[-0.125,0.125,0.125]], 37 | [[0.5,0.0,0.0],[0.5,0.0,0.0]], 38 | [[0.125,-0.125,-0.125]], 39 | [[0.0,-0.25,-0.25],[0.0,0.25,0.25]], 40 | [[-0.125,-0.125,0.125],[0.125,-0.125,-0.125]], 41 | [[0.0,-0.5,0.0],[0.25,0.25,0.25],[0.125,0.125,0.125]], 42 | [[0.125,-0.125,0.125],[0.125,-0.125,-0.125]], 43 | [[0.0,0.0,-0.5],[0.25,0.25,0.25],[-0.125,-0.125,-0.125]], 44 | [[-0.125,-0.125,0.125],[0.125,-0.125,0.125],[0.125,-0.125,-0.125]], 45 | [[-0.125,-0.125,-0.125],[-0.25,-0.25,-0.25],[0.25,0.25,0.25],[0.125,0.125,0.125]], 46 | [[-0.125,0.125,0.125],[0.125,-0.125,-0.125]], 47 | [[0.0,-0.25,-0.25],[0.0,0.25,0.25],[-0.125,0.125,0.125]], 48 | [[-0.25,0.0,0.25],[-0.25,0.0,0.25],[0.125,-0.125,-0.125]], 49 | [[0.125,0.125,0.125],[0.375,0.375,0.375],[0.0,-0.25,0.25],[-0.25,0.0,0.25]], 50 | [[0.125,-0.125,-0.125],[0.25,-0.25,0.0],[0.25,-0.25,0.0]], 51 | [[0.375,0.375,0.375],[0.0,0.25,-0.25],[-0.125,-0.125,-0.125],[-0.25,0.25,0.0]], 52 | [[-0.5,0.0,0.0],[-0.125,-0.125,-0.125],[-0.25,-0.25,-0.25],[0.125,0.125,0.125]], 53 | [[-0.5,0.0,0.0],[-0.125,-0.125,-0.125],[-0.25,-0.25,-0.25]], 54 | [[0.125,-0.125,0.125]], 55 | [[0.125,0.125,0.125],[0.125,-0.125,0.125]], 56 | [[0.0,-0.25,0.25],[0.0,0.25,-0.25]], 57 | [[0.0,-0.5,0.0],[0.125,0.125,-0.125],[0.25,0.25,-0.25]], 58 | [[0.125,-0.125,0.125],[0.125,-0.125,0.125]], 59 | [[0.125,-0.125,0.125],[-0.25,-0.0,-0.25],[0.25,0.0,0.25]], 60 | [[0.0,-0.25,0.25],[0.0,0.25,-0.25],[0.125,-0.125,0.125]], 61 | [[-0.375,-0.375,0.375],[-0.0,0.25,0.25],[0.125,0.125,-0.125],[-0.25,-0.0,-0.25]], 62 | [[-0.125,0.125,0.125],[0.125,-0.125,0.125]], 63 | [[0.125,0.125,0.125],[0.125,-0.125,0.125],[-0.125,0.125,0.125]], 64 | [[-0.0,0.0,0.5],[-0.25,-0.25,0.25],[-0.125,-0.125,0.125]], 65 | [[0.25,0.25,-0.25],[0.25,0.25,-0.25],[0.125,0.125,-0.125],[-0.125,-0.125,0.125]], 66 | [[0.125,-0.125,0.125],[0.25,-0.25,0.0],[0.25,-0.25,0.0]], 67 | [[0.5,0.0,0.0],[0.25,-0.25,0.25],[-0.125,0.125,-0.125],[0.125,-0.125,0.125]], 68 | [[0.0,0.25,-0.25],[0.375,-0.375,-0.375],[-0.125,0.125,0.125],[0.25,0.25,0.0]], 69 | [[-0.5,0.0,0.0],[-0.25,-0.25,0.25],[-0.125,-0.125,0.125]], 70 | [[0.25,-0.25,0.0],[-0.25,0.25,0.0]], 71 | [[0.0,0.5,0.0],[-0.25,0.25,0.25],[0.125,-0.125,-0.125]], 72 | [[0.0,0.5,0.0],[0.125,-0.125,0.125],[-0.25,0.25,-0.25]], 73 | [[0.0,0.5,0.0],[0.0,-0.5,0.0]], 74 | [[0.25,-0.25,0.0],[-0.25,0.25,0.0],[0.125,-0.125,0.125]], 75 | [[-0.375,-0.375,-0.375],[-0.25,0.0,0.25],[-0.125,-0.125,-0.125],[-0.25,0.25,0.0]], 76 | [[0.125,0.125,0.125],[0.0,-0.5,0.0],[-0.25,-0.25,-0.25],[-0.125,-0.125,-0.125]], 77 | [[0.0,-0.5,0.0],[-0.25,-0.25,-0.25],[-0.125,-0.125,-0.125]], 78 | [[-0.125,0.125,0.125],[0.25,-0.25,0.0],[-0.25,0.25,0.0]], 79 | [[0.0,0.5,0.0],[0.25,0.25,-0.25],[-0.125,-0.125,0.125],[-0.125,-0.125,0.125]], 80 | [[-0.375,0.375,-0.375],[-0.25,-0.25,0.0],[-0.125,0.125,-0.125],[-0.25,0.0,0.25]], 81 | [[0.0,0.5,0.0],[0.25,0.25,-0.25],[-0.125,-0.125,0.125]], 82 | [[0.25,-0.25,0.0],[-0.25,0.25,0.0],[0.25,-0.25,0.0],[0.25,-0.25,0.0]], 83 | [[-0.25,-0.25,0.0],[-0.25,-0.25,0.0],[-0.125,-0.125,0.125]], 84 | [[0.125,0.125,0.125],[-0.25,-0.25,0.0],[-0.25,-0.25,0.0]], 85 | [[-0.25,-0.25,0.0],[-0.25,-0.25,0.0]], 86 | [[-0.125,-0.125,0.125]], 87 | [[0.125,0.125,0.125],[-0.125,-0.125,0.125]], 88 | [[-0.125,-0.125,0.125],[-0.125,-0.125,0.125]], 89 | [[-0.125,-0.125,0.125],[-0.25,-0.25,0.0],[0.25,0.25,-0.0]], 90 | [[0.0,-0.25,0.25],[0.0,-0.25,0.25]], 91 | [[0.0,0.0,0.5],[0.25,-0.25,0.25],[0.125,-0.125,0.125]], 92 | [[0.0,-0.25,0.25],[0.0,-0.25,0.25],[-0.125,-0.125,0.125]], 93 | [[0.375,-0.375,0.375],[0.0,-0.25,-0.25],[-0.125,0.125,-0.125],[0.25,0.25,0.0]], 94 | [[-0.125,-0.125,0.125],[-0.125,0.125,0.125]], 95 | [[0.125,0.125,0.125],[-0.125,-0.125,0.125],[-0.125,0.125,0.125]], 96 | [[-0.125,-0.125,0.125],[-0.25,0.0,0.25],[-0.25,0.0,0.25]], 97 | [[0.5,0.0,0.0],[-0.25,-0.25,0.25],[-0.125,-0.125,0.125],[-0.125,-0.125,0.125]], 98 | 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[[-0.25,-0.25,0.0],[-0.25,-0.25,0.0]], 215 | [[0.125,0.125,0.125],[-0.25,-0.25,0.0],[-0.25,-0.25,0.0]], 216 | [[-0.25,-0.25,0.0],[-0.25,-0.25,0.0],[-0.125,-0.125,0.125]], 217 | [[-0.25,-0.25,0.0],[-0.25,-0.25,0.0],[-0.25,-0.25,0.0],[0.25,0.25,-0.0]], 218 | [[0.0,0.5,0.0],[0.25,0.25,-0.25],[-0.125,-0.125,0.125]], 219 | [[-0.375,0.375,-0.375],[-0.25,-0.25,0.0],[-0.125,0.125,-0.125],[-0.25,0.0,0.25]], 220 | [[0.0,0.5,0.0],[0.25,0.25,-0.25],[-0.125,-0.125,0.125],[-0.125,-0.125,0.125]], 221 | [[-0.125,0.125,0.125],[0.25,-0.25,0.0],[-0.25,0.25,0.0]], 222 | [[0.0,-0.5,0.0],[-0.25,-0.25,-0.25],[-0.125,-0.125,-0.125]], 223 | [[0.125,0.125,0.125],[0.0,-0.5,0.0],[-0.25,-0.25,-0.25],[-0.125,-0.125,-0.125]], 224 | [[-0.375,-0.375,-0.375],[-0.25,0.0,0.25],[-0.125,-0.125,-0.125],[-0.25,0.25,0.0]], 225 | [[0.25,-0.25,0.0],[-0.25,0.25,0.0],[0.125,-0.125,0.125]], 226 | [[0.0,0.5,0.0],[0.0,-0.5,0.0]], 227 | [[0.0,0.5,0.0],[0.125,-0.125,0.125],[-0.25,0.25,-0.25]], 228 | [[0.0,0.5,0.0],[-0.25,0.25,0.25],[0.125,-0.125,-0.125]], 229 | [[0.25,-0.25,0.0],[-0.25,0.25,0.0]], 230 | [[-0.5,0.0,0.0],[-0.25,-0.25,0.25],[-0.125,-0.125,0.125]], 231 | [[0.0,0.25,-0.25],[0.375,-0.375,-0.375],[-0.125,0.125,0.125],[0.25,0.25,0.0]], 232 | [[0.5,0.0,0.0],[0.25,-0.25,0.25],[-0.125,0.125,-0.125],[0.125,-0.125,0.125]], 233 | [[0.125,-0.125,0.125],[0.25,-0.25,0.0],[0.25,-0.25,0.0]], 234 | [[0.25,0.25,-0.25],[0.25,0.25,-0.25],[0.125,0.125,-0.125],[-0.125,-0.125,0.125]], 235 | [[-0.0,0.0,0.5],[-0.25,-0.25,0.25],[-0.125,-0.125,0.125]], 236 | [[0.125,0.125,0.125],[0.125,-0.125,0.125],[-0.125,0.125,0.125]], 237 | [[-0.125,0.125,0.125],[0.125,-0.125,0.125]], 238 | [[-0.375,-0.375,0.375],[-0.0,0.25,0.25],[0.125,0.125,-0.125],[-0.25,-0.0,-0.25]], 239 | [[0.0,-0.25,0.25],[0.0,0.25,-0.25],[0.125,-0.125,0.125]], 240 | [[0.125,-0.125,0.125],[-0.25,-0.0,-0.25],[0.25,0.0,0.25]], 241 | [[0.125,-0.125,0.125],[0.125,-0.125,0.125]], 242 | [[0.0,-0.5,0.0],[0.125,0.125,-0.125],[0.25,0.25,-0.25]], 243 | [[0.0,-0.25,0.25],[0.0,0.25,-0.25]], 244 | [[0.125,0.125,0.125],[0.125,-0.125,0.125]], 245 | [[0.125,-0.125,0.125]], 246 | [[-0.5,0.0,0.0],[-0.125,-0.125,-0.125],[-0.25,-0.25,-0.25]], 247 | [[-0.5,0.0,0.0],[-0.125,-0.125,-0.125],[-0.25,-0.25,-0.25],[0.125,0.125,0.125]], 248 | [[0.375,0.375,0.375],[0.0,0.25,-0.25],[-0.125,-0.125,-0.125],[-0.25,0.25,0.0]], 249 | [[0.125,-0.125,-0.125],[0.25,-0.25,0.0],[0.25,-0.25,0.0]], 250 | [[0.125,0.125,0.125],[0.375,0.375,0.375],[0.0,-0.25,0.25],[-0.25,0.0,0.25]], 251 | [[-0.25,0.0,0.25],[-0.25,0.0,0.25],[0.125,-0.125,-0.125]], 252 | [[0.0,-0.25,-0.25],[0.0,0.25,0.25],[-0.125,0.125,0.125]], 253 | [[-0.125,0.125,0.125],[0.125,-0.125,-0.125]], 254 | [[-0.125,-0.125,-0.125],[-0.25,-0.25,-0.25],[0.25,0.25,0.25],[0.125,0.125,0.125]], 255 | [[-0.125,-0.125,0.125],[0.125,-0.125,0.125],[0.125,-0.125,-0.125]], 256 | [[0.0,0.0,-0.5],[0.25,0.25,0.25],[-0.125,-0.125,-0.125]], 257 | [[0.125,-0.125,0.125],[0.125,-0.125,-0.125]], 258 | [[0.0,-0.5,0.0],[0.25,0.25,0.25],[0.125,0.125,0.125]], 259 | [[-0.125,-0.125,0.125],[0.125,-0.125,-0.125]], 260 | [[0.0,-0.25,-0.25],[0.0,0.25,0.25]], 261 | [[0.125,-0.125,-0.125]], 262 | [[0.5,0.0,0.0],[0.5,0.0,0.0]], 263 | [[-0.5,0.0,0.0],[-0.25,0.25,0.25],[-0.125,0.125,0.125]], 264 | [[0.5,0.0,0.0],[0.25,-0.25,0.25],[-0.125,0.125,-0.125]], 265 | [[0.25,-0.25,0.0],[0.25,-0.25,0.0]], 266 | [[0.5,0.0,0.0],[-0.25,-0.25,0.25],[-0.125,-0.125,0.125]], 267 | [[-0.25,0.0,0.25],[-0.25,0.0,0.25]], 268 | [[0.125,0.125,0.125],[-0.125,0.125,0.125]], 269 | [[-0.125,0.125,0.125]], 270 | [[0.5,0.0,-0.0],[0.25,0.25,0.25],[0.125,0.125,0.125]], 271 | [[0.125,-0.125,0.125],[-0.125,-0.125,0.125]], 272 | [[-0.25,-0.0,-0.25],[0.25,0.0,0.25]], 273 | [[0.125,-0.125,0.125]], 274 | [[-0.25,-0.25,0.0],[0.25,0.25,-0.0]], 275 | [[-0.125,-0.125,0.125]], 276 | [[0.125,0.125,0.125]], 277 | [[0,0,0]]] 278 | 279 | 280 | def compute_surface_distances(mask_gt, mask_pred, spacing_mm): 281 | """Compute closest distances from all surface points to the other surface. 282 | 283 | Finds all surface elements "surfels" in the ground truth mask `mask_gt` and 284 | the predicted mask `mask_pred`, computes their area in mm^2 and the distance 285 | to the closest point on the other surface. It returns two sorted lists of 286 | distances together with the corresponding surfel areas. If one of the masks 287 | is empty, the corresponding lists are empty and all distances in the other 288 | list are `inf` 289 | 290 | Args: 291 | mask_gt: 3-dim Numpy array of type bool. The ground truth mask. 292 | mask_pred: 3-dim Numpy array of type bool. The predicted mask. 293 | spacing_mm: 3-element list-like structure. Voxel spacing in x0, x1 and x2 294 | direction 295 | 296 | Returns: 297 | A dict with 298 | "distances_gt_to_pred": 1-dim numpy array of type float. The distances in mm 299 | from all ground truth surface elements to the predicted surface, 300 | sorted from smallest to largest 301 | "distances_pred_to_gt": 1-dim numpy array of type float. The distances in mm 302 | from all predicted surface elements to the ground truth surface, 303 | sorted from smallest to largest 304 | "surfel_areas_gt": 1-dim numpy array of type float. The area in mm^2 of 305 | the ground truth surface elements in the same order as 306 | distances_gt_to_pred 307 | "surfel_areas_pred": 1-dim numpy array of type float. The area in mm^2 of 308 | the predicted surface elements in the same order as 309 | distances_pred_to_gt 310 | 311 | """ 312 | 313 | # compute the area for all 256 possible surface elements 314 | # (given a 2x2x2 neighbourhood) according to the spacing_mm 315 | neighbour_code_to_surface_area = np.zeros([256]) 316 | for code in range(256): 317 | normals = np.array(neighbour_code_to_normals[code]) 318 | sum_area = 0 319 | for normal_idx in range(normals.shape[0]): 320 | # normal vector 321 | n = np.zeros([3]) 322 | n[0] = normals[normal_idx,0] * spacing_mm[1] * spacing_mm[2] 323 | n[1] = normals[normal_idx,1] * spacing_mm[0] * spacing_mm[2] 324 | n[2] = normals[normal_idx,2] * spacing_mm[0] * spacing_mm[1] 325 | area = np.linalg.norm(n) 326 | sum_area += area 327 | neighbour_code_to_surface_area[code] = sum_area 328 | 329 | # compute the bounding box of the masks to trim 330 | # the volume to the smallest possible processing subvolume 331 | mask_all = mask_gt | mask_pred 332 | bbox_min = np.zeros(3, np.int64) 333 | bbox_max = np.zeros(3, np.int64) 334 | 335 | # max projection to the x0-axis 336 | proj_0 = np.max(np.max(mask_all, axis=2), axis=1) 337 | idx_nonzero_0 = np.nonzero(proj_0)[0] 338 | if len(idx_nonzero_0) == 0: 339 | return {"distances_gt_to_pred": np.array([]), 340 | "distances_pred_to_gt": np.array([]), 341 | "surfel_areas_gt": np.array([]), 342 | "surfel_areas_pred": np.array([])} 343 | 344 | bbox_min[0] = np.min(idx_nonzero_0) 345 | bbox_max[0] = np.max(idx_nonzero_0) 346 | 347 | # max projection to the x1-axis 348 | proj_1 = np.max(np.max(mask_all, axis=2), axis=0) 349 | idx_nonzero_1 = np.nonzero(proj_1)[0] 350 | bbox_min[1] = np.min(idx_nonzero_1) 351 | bbox_max[1] = np.max(idx_nonzero_1) 352 | 353 | # max projection to the x2-axis 354 | proj_2 = np.max(np.max(mask_all, axis=1), axis=0) 355 | idx_nonzero_2 = np.nonzero(proj_2)[0] 356 | bbox_min[2] = np.min(idx_nonzero_2) 357 | bbox_max[2] = np.max(idx_nonzero_2) 358 | 359 | # print("bounding box min = {}".format(bbox_min)) 360 | # print("bounding box max = {}".format(bbox_max)) 361 | 362 | # crop the processing subvolume. 363 | # we need to zeropad the cropped region with 1 voxel at the lower, 364 | # the right and the back side. This is required to obtain the "full" 365 | # convolution result with the 2x2x2 kernel 366 | cropmask_gt = np.zeros((bbox_max - bbox_min)+2, np.uint8) 367 | cropmask_pred = np.zeros((bbox_max - bbox_min)+2, np.uint8) 368 | 369 | cropmask_gt[0:-1, 0:-1, 0:-1] = mask_gt[bbox_min[0]:bbox_max[0]+1, 370 | bbox_min[1]:bbox_max[1]+1, 371 | bbox_min[2]:bbox_max[2]+1] 372 | 373 | cropmask_pred[0:-1, 0:-1, 0:-1] = mask_pred[bbox_min[0]:bbox_max[0]+1, 374 | bbox_min[1]:bbox_max[1]+1, 375 | bbox_min[2]:bbox_max[2]+1] 376 | 377 | # compute the neighbour code (local binary pattern) for each voxel 378 | # the resultsing arrays are spacially shifted by minus half a voxel in each axis. 379 | # i.e. the points are located at the corners of the original voxels 380 | kernel = np.array([[[128,64], 381 | [32,16]], 382 | [[8,4], 383 | [2,1]]]) 384 | neighbour_code_map_gt = scipy.ndimage.filters.correlate(cropmask_gt.astype(np.uint8), kernel, mode="constant", cval=0) 385 | neighbour_code_map_pred = scipy.ndimage.filters.correlate(cropmask_pred.astype(np.uint8), kernel, mode="constant", cval=0) 386 | 387 | # create masks with the surface voxels 388 | borders_gt = ((neighbour_code_map_gt != 0) & (neighbour_code_map_gt != 255)) 389 | borders_pred = ((neighbour_code_map_pred != 0) & (neighbour_code_map_pred != 255)) 390 | 391 | # compute the distance transform (closest distance of each voxel to the surface voxels) 392 | if borders_gt.any(): 393 | distmap_gt = scipy.ndimage.morphology.distance_transform_edt(~borders_gt, sampling=spacing_mm) 394 | else: 395 | distmap_gt = np.Inf * np.ones(borders_gt.shape) 396 | 397 | if borders_pred.any(): 398 | distmap_pred = scipy.ndimage.morphology.distance_transform_edt(~borders_pred, sampling=spacing_mm) 399 | else: 400 | distmap_pred = np.Inf * np.ones(borders_pred.shape) 401 | 402 | # compute the area of each surface element 403 | surface_area_map_gt = neighbour_code_to_surface_area[neighbour_code_map_gt] 404 | surface_area_map_pred = neighbour_code_to_surface_area[neighbour_code_map_pred] 405 | 406 | # create a list of all surface elements with distance and area 407 | distances_gt_to_pred = distmap_pred[borders_gt] 408 | distances_pred_to_gt = distmap_gt[borders_pred] 409 | surfel_areas_gt = surface_area_map_gt[borders_gt] 410 | surfel_areas_pred = surface_area_map_pred[borders_pred] 411 | 412 | # sort them by distance 413 | if distances_gt_to_pred.shape != (0,): 414 | sorted_surfels_gt = np.array(sorted(zip(distances_gt_to_pred, surfel_areas_gt))) 415 | distances_gt_to_pred = sorted_surfels_gt[:,0] 416 | surfel_areas_gt = sorted_surfels_gt[:,1] 417 | 418 | if distances_pred_to_gt.shape != (0,): 419 | sorted_surfels_pred = np.array(sorted(zip(distances_pred_to_gt, surfel_areas_pred))) 420 | distances_pred_to_gt = sorted_surfels_pred[:,0] 421 | surfel_areas_pred = sorted_surfels_pred[:,1] 422 | 423 | 424 | return {"distances_gt_to_pred": distances_gt_to_pred, 425 | "distances_pred_to_gt": distances_pred_to_gt, 426 | "surfel_areas_gt": surfel_areas_gt, 427 | "surfel_areas_pred": surfel_areas_pred} 428 | 429 | 430 | def compute_average_surface_distance(surface_distances): 431 | distances_gt_to_pred = surface_distances["distances_gt_to_pred"] 432 | distances_pred_to_gt = surface_distances["distances_pred_to_gt"] 433 | surfel_areas_gt = surface_distances["surfel_areas_gt"] 434 | surfel_areas_pred = surface_distances["surfel_areas_pred"] 435 | average_distance_gt_to_pred = np.sum( distances_gt_to_pred * surfel_areas_gt) / np.sum(surfel_areas_gt) 436 | average_distance_pred_to_gt = np.sum( distances_pred_to_gt * surfel_areas_pred) / np.sum(surfel_areas_pred) 437 | return (average_distance_gt_to_pred, average_distance_pred_to_gt) 438 | 439 | def compute_robust_hausdorff(surface_distances, percent): 440 | distances_gt_to_pred = surface_distances["distances_gt_to_pred"] 441 | distances_pred_to_gt = surface_distances["distances_pred_to_gt"] 442 | surfel_areas_gt = surface_distances["surfel_areas_gt"] 443 | surfel_areas_pred = surface_distances["surfel_areas_pred"] 444 | if len(distances_gt_to_pred) > 0: 445 | surfel_areas_cum_gt = np.cumsum(surfel_areas_gt) / np.sum(surfel_areas_gt) 446 | idx = np.searchsorted(surfel_areas_cum_gt, percent/100.0) 447 | perc_distance_gt_to_pred = distances_gt_to_pred[min(idx, len(distances_gt_to_pred)-1)] 448 | else: 449 | perc_distance_gt_to_pred = np.Inf 450 | 451 | if len(distances_pred_to_gt) > 0: 452 | surfel_areas_cum_pred = np.cumsum(surfel_areas_pred) / np.sum(surfel_areas_pred) 453 | idx = np.searchsorted(surfel_areas_cum_pred, percent/100.0) 454 | perc_distance_pred_to_gt = distances_pred_to_gt[min(idx, len(distances_pred_to_gt)-1)] 455 | else: 456 | perc_distance_pred_to_gt = np.Inf 457 | 458 | return max( perc_distance_gt_to_pred, perc_distance_pred_to_gt) 459 | 460 | def compute_surface_overlap_at_tolerance(surface_distances, tolerance_mm): 461 | distances_gt_to_pred = surface_distances["distances_gt_to_pred"] 462 | distances_pred_to_gt = surface_distances["distances_pred_to_gt"] 463 | surfel_areas_gt = surface_distances["surfel_areas_gt"] 464 | surfel_areas_pred = surface_distances["surfel_areas_pred"] 465 | rel_overlap_gt = np.sum(surfel_areas_gt[distances_gt_to_pred <= tolerance_mm]) / np.sum(surfel_areas_gt) 466 | rel_overlap_pred = np.sum(surfel_areas_pred[distances_pred_to_gt <= tolerance_mm]) / np.sum(surfel_areas_pred) 467 | return (rel_overlap_gt, rel_overlap_pred) 468 | 469 | def compute_surface_dice_at_tolerance(surface_distances, tolerance_mm): 470 | distances_gt_to_pred = surface_distances["distances_gt_to_pred"] 471 | distances_pred_to_gt = surface_distances["distances_pred_to_gt"] 472 | surfel_areas_gt = surface_distances["surfel_areas_gt"] 473 | surfel_areas_pred = surface_distances["surfel_areas_pred"] 474 | overlap_gt = np.sum(surfel_areas_gt[distances_gt_to_pred <= tolerance_mm]) 475 | overlap_pred = np.sum(surfel_areas_pred[distances_pred_to_gt <= tolerance_mm]) 476 | surface_dice = (overlap_gt + overlap_pred) / ( 477 | np.sum(surfel_areas_gt) + np.sum(surfel_areas_pred)) 478 | return surface_dice 479 | 480 | 481 | def compute_dice_coefficient(mask_gt, mask_pred): 482 | """Compute soerensen-dice coefficient. 483 | 484 | compute the soerensen-dice coefficient between the ground truth mask `mask_gt` 485 | and the predicted mask `mask_pred`. 486 | 487 | Args: 488 | mask_gt: 3-dim Numpy array of type bool. The ground truth mask. 489 | mask_pred: 3-dim Numpy array of type bool. The predicted mask. 490 | 491 | Returns: 492 | the dice coeffcient as float. If both masks are empty, the result is NaN 493 | """ 494 | volume_sum = mask_gt.sum() + mask_pred.sum() 495 | if volume_sum == 0: 496 | return np.NaN 497 | volume_intersect = (mask_gt & mask_pred).sum() 498 | return 2*volume_intersect / volume_sum 499 | 500 | -------------------------------------------------------------------------------- /CVPR25_iter_eval.py: -------------------------------------------------------------------------------- 1 | """ 2 | The code was adapted from the CVPR24 Segment Anything in Medical Images on a Laptop Challenge 3 | https://www.codabench.org/competitions/1847/ 4 | 5 | pip install connected-components-3d 6 | pip install cupy-cuda12x 7 | pip install cucim-cu12 8 | 9 | 10 | The testing images will be evaluated one by one. 11 | 12 | Folder structure: 13 | CVPR25_iter_eval.py 14 | --docker_folder path # submitted docker containers from participants 15 | - docker_dir 16 | - teamname_1.tar.gz 17 | - teamname_2.tar.gz 18 | - ... 19 | --test_img_path # test images 20 | - imgs 21 | - case1.npz # test image 22 | - case2.npz 23 | - ... 24 | --save_path # segmentation results 25 | - output 26 | - case1.npz # segmentation file name is the same as the testing image name 27 | - case2.npz 28 | - ... 29 | --validation_gts_path # path to validation / test set GT files 30 | - Contains the npz files with the same name as the images but only 'gts' key is available in each file instead of storing it in the image itself. This is done to prevent label leakage during the challenge. 31 | - validation_gts 32 | - case1.npz # file containing only the 'gts' key 33 | - case2.npz 34 | - ... 35 | --verbose 36 | - Whether to have a more detailed output, e.g. coordinates of generated clicks 37 | 38 | 39 | This script is designed for evaluating docker submissions for the CVPR25: Foundation Models for Interactive 3D Biomedical Image Segmentation Challenge Challenge 40 | 41 | ########################################################## 42 | ######### Docker Submission Evaluation Process ########### 43 | ########################################################## 44 | Submissions for the CVPR 2025: Foundation Models for Interactive 3D Biomedical Image Segmentation Challenge will be evaluated using an iterative refinement approach. 45 | Each participant's Docker container will be tested on a set of medical images provided as .npz files. 46 | The evaluation process follows these key steps: 47 | - Initial Prediction: Image + Bounding Box Prompt (1 prediction) 48 | - Each test case begins with a bounding box prompt, specified in the 'bbox' key of the test image. This serves as the starting point for the segmentation. 49 | - Iterative Click Refinements: Image + Bounding Box + 1-5 Clicks (5 predictions) 50 | - After the initial segmentation, we iteratively simulate 5 refinement clicks to address segmentation errors. These clicks are automatically generated based on the center of the largest error region in the current prediction: 51 | - If the center of the largest error is an undersegmentation, we simulate and place a foreground click. 52 | - If the center of the largest error is an oversegmentation, we simulate and place a background click. 53 | - The clicks are stored in the clicks key of the 'npz' file and progressively updated during the second step of the evaluation. 54 | 55 | ############################################################### 56 | ######### How are interactions (bbox, clicks) stored? ######### 57 | ############################################################### 58 | The interactions are stored in the 'bbox' and 'clicks' keys of each input .npz image. 59 | - The bounding box is stored in the 'bbox' key as a list of dictionaries [{'z_min': 27, 'z_max': 396, 'z_mid': 311, 'z_mid_x_min': 175, 'z_mid_y_min': 94, 'z_mid_x_max': 278, 'z_mid_y_max': 233}, ...] containing bbox coordinates for each class. 60 | - The clicks are provided in the 'clicks' key as a list of dictionaries [{'fg': [click_fg_1, clicks_fg_2,...], 'bg': [click_bg_1, click_bg_2,...]}, ...] 61 | where click_fg_i and click_bg_i are 3-element arrays with the 3D click coordinates [x, y, z]. 62 | 63 | ####################################### 64 | ######### Performance Metrics ######### 65 | ####################################### 66 | For each image, multi-class segmentation quality is evaluated using: 67 | - Dice Similarity Coefficient (DSC) and Normalized Surface Dice (NSD), calculated iteratively over the 6 steps (bounding box + 5 clicks). 68 | - AUC (Area Under the Curve) for DSC and NSD to measure cumulative improvement with more interactions. 69 | - Final DSC and NSD after all interactions. 70 | - Inference Time averaged over all 6 steps. 71 | 72 | ########################## 73 | ######### Output ######### 74 | ########################## 75 | Results are saved in .npz format with metrics compiled into a CSV file for each submission. 5 metrics are stored: DSC_AUC, NSD_AUC, Final_DSC, Final_NSD, Inference Time. 76 | 77 | 78 | ################################ 79 | ######### Script Steps ######### 80 | ################################ 81 | This script executes the following steps: 82 | 1. Docker Submission Handling: 83 | - Loads docker containers submitted by participants. 84 | - Executes inference for each test image using the participant's docker container. Images are infered one by one. 85 | 86 | 2. Iterative Refinement: 87 | - The initial bounding box prediction is refined iteratively by simulating user clicks at the centers of segmentation errors for each class in the image. 88 | - The Euclidean Distance Transform (EDT) is computed for error regions to identify the distance to the boundary of each error component, 89 | ensuring clicks are placed at locations at the center of the largest error for refinement. 90 | - For each image, the docker is run 6 times for inference: 91 | - 1) Bounding Box initial prediction 92 | - 2)-6) Click refinement predictions (each new click is placed in the center of the largest error component) 93 | - If the center of the largest error is part of the background --> a background click is placed 94 | - Otherwise, a foreground click is placed 95 | - Steps 1)-6) are done in parallel for all segmentation classes in 6 interaction steps (6 docker runs) 96 | 97 | 3. GPU vs. CPU Computation: 98 | - If a GPU is available, the script uses `cupy` and `cucim` for accelerated EDT computation. 99 | - For CPU-only environments, `scipy.ndimage.distance_transform_edt` is used as a fallback. 100 | 101 | 4. Metrics Calculation: 102 | - Computes multi-class DSC and NSD for each image. 103 | - For the final metrics, the AUC (Area Under the Curve) for the DSC and NSD are computed for iterative improvement across the 6 interactive iterations. 104 | - The AUC quantifies the cumulative performance improvement over the 6 successive iterations (bbox + 5 clicks) providing a holistic view of the segmentation refinement process. 105 | - The final DSC and NSD after all 6 interactive steps are also computed. 106 | - These metrics reflect the final segmentation quality achieved after all refinements, indicating the model's final performance. 107 | - The last metric is the inference time which is the average inference time over the 6 interactive steps. 108 | 109 | 5. Output: 110 | - Segmentation results are saved in the specified output directory. 111 | - Final prediction in the 'segs' key 112 | - Intermediate prediction in the 'all_segs' key 113 | - Metrics for each test case are compiled into a CSV file. 114 | 115 | ################################# 116 | ############## Misc############## 117 | ################################# 118 | - The input image also contains the 'prev_pred' key which stores the prediction from the previous iteration. This is used only to help with submission that are using the previous prediction as an additional input and is not 119 | a mandatory input. 120 | """ 121 | 122 | import os 123 | join = os.path.join 124 | import shutil 125 | import time 126 | import torch 127 | import argparse 128 | from collections import OrderedDict 129 | import pandas as pd 130 | import numpy as np 131 | import traceback 132 | 133 | from scipy.ndimage import distance_transform_edt 134 | import cc3d 135 | from SurfaceDice import compute_surface_distances, compute_surface_dice_at_tolerance, compute_dice_coefficient 136 | from scipy import integrate 137 | from tqdm import tqdm 138 | 139 | # Taken from CVPR24 challenge code with change to np.unique 140 | def compute_multi_class_dsc(gt, seg): 141 | dsc = [] 142 | for i in np.sort(pd.unique(gt.ravel()))[1:]: # skip bg 143 | gt_i = gt == i 144 | seg_i = seg == i 145 | dsc.append(compute_dice_coefficient(gt_i, seg_i)) 146 | return np.mean(dsc) 147 | 148 | # Taken from CVPR24 challenge code with change to np.unique 149 | def compute_multi_class_nsd(gt, seg, spacing, tolerance=2.0): 150 | nsd = [] 151 | for i in np.sort(pd.unique(gt.ravel()))[1:]: # skip bg 152 | gt_i = gt == i 153 | seg_i = seg == i 154 | surface_distance = compute_surface_distances( 155 | gt_i, seg_i, spacing_mm=spacing 156 | ) 157 | nsd.append(compute_surface_dice_at_tolerance(surface_distance, tolerance)) 158 | return np.mean(nsd) 159 | 160 | def patched_np_load(*args, **kwargs): 161 | with np.load(*args, **kwargs) as f: 162 | return dict(f) 163 | 164 | def sample_coord(edt): 165 | # Find all coordinates with max EDT value 166 | np.random.seed(42) 167 | 168 | max_val = edt.max() 169 | max_coords = np.argwhere(edt == max_val) 170 | 171 | # Uniformly choose one of them 172 | chosen_index = max_coords[np.random.choice(len(max_coords))] 173 | 174 | center = tuple(chosen_index) 175 | return center 176 | 177 | # Compute the EDT with same shape as the image 178 | def compute_edt(error_component): 179 | # Get bounding box of the largest error component to limit computation 180 | coords = np.argwhere(error_component) 181 | min_coords = coords.min(axis=0) 182 | max_coords = coords.max(axis=0) + 1 183 | 184 | crop_shape = max_coords - min_coords 185 | 186 | # Compute padding (25% of crop size in each dimension) 187 | padding = np.maximum((crop_shape * 0.25).astype(int), 1) 188 | 189 | 190 | # Define new padded shape 191 | padded_shape = crop_shape + 2 * padding 192 | 193 | # Create new empty array with padding 194 | center_crop = np.zeros(padded_shape, dtype=np.uint8) 195 | 196 | # Fill center region with actual cropped data 197 | center_crop[ 198 | padding[0]:padding[0] + crop_shape[0], 199 | padding[1]:padding[1] + crop_shape[1], 200 | padding[2]:padding[2] + crop_shape[2] 201 | ] = error_component[ 202 | min_coords[0]:max_coords[0], 203 | min_coords[1]:max_coords[1], 204 | min_coords[2]:max_coords[2] 205 | ] 206 | 207 | large_roi = False 208 | if center_crop.shape[0] * center_crop.shape[1] * center_crop.shape[2] > 60000000: 209 | from skimage.measure import block_reduce 210 | print(f'ROI too large {center_crop.shape} --> 2x downsampling for EDT') 211 | center_crop = block_reduce(center_crop, block_size=(2, 2, 2), func=np.max) 212 | large_roi = True 213 | 214 | # Compute EDT on the padded array 215 | if torch.cuda.is_available() and not large_roi: # GPU available 216 | import cupy as cp 217 | from cucim.core.operations import morphology 218 | error_mask_cp = cp.array(center_crop) 219 | edt_cp = morphology.distance_transform_edt(error_mask_cp, return_distances=True) 220 | edt = cp.asnumpy(edt_cp) 221 | else: # CPU available only 222 | edt = distance_transform_edt(center_crop) 223 | 224 | if large_roi: # upsample 225 | edt = edt.repeat(2, axis=0).repeat(2, axis=1).repeat(2, axis=2) 226 | 227 | # Crop out the center (remove padding) 228 | dist_cropped = edt[ 229 | padding[0]:padding[0] + crop_shape[0], 230 | padding[1]:padding[1] + crop_shape[1], 231 | padding[2]:padding[2] + crop_shape[2] 232 | ] 233 | 234 | # Create full-sized EDT result array and splat back 235 | dist_full = np.zeros_like(error_component, dtype=dist_cropped.dtype) 236 | dist_full[ 237 | min_coords[0]:max_coords[0], 238 | min_coords[1]:max_coords[1], 239 | min_coords[2]:max_coords[2] 240 | ] = dist_cropped 241 | 242 | dist_transformed = dist_full 243 | 244 | return dist_transformed 245 | 246 | parser = argparse.ArgumentParser('Segmentation iterative refinement with clicks eavluation for docker containers', add_help=False) 247 | parser.add_argument('-i', '--test_img_path', default='3D_val_npz', type=str, help='testing data path') 248 | parser.add_argument('-o','--save_path', default='./seg', type=str, help='segmentation output path') 249 | parser.add_argument('-d','--docker_folder_path', default='./team_docker', type=str, help='team docker path') 250 | parser.add_argument('-val_gts','--validation_gts_path', default='3D_val_gt_interactive_seg', type=str, help='path to validation set (or final test set) GT files') 251 | parser.add_argument('-v','--verbose', default=False, action='store_true', help="Verbose output, e.g., print coordinates of generated clicks") 252 | 253 | args = parser.parse_args() 254 | 255 | test_img_path = args.test_img_path 256 | save_path = args.save_path 257 | docker_path = args.docker_folder_path 258 | validation_gts_path = args.validation_gts_path 259 | verbose = args.verbose 260 | 261 | if not os.path.exists(validation_gts_path): 262 | validation_gts_path = None 263 | print('[WARNING] Validation path does not exist for your GT data! Make sure you supplied the correct path or your .npz inputs have a gts key!') 264 | 265 | input_temp = './inputs/' 266 | output_temp = './outputs' 267 | os.makedirs(save_path, exist_ok=True) 268 | 269 | dockers = sorted(os.listdir(docker_path)) 270 | test_cases = sorted(os.listdir(test_img_path)) 271 | 272 | for docker in dockers: 273 | try: 274 | # create temp folers for inference one-by-one 275 | if os.path.exists(input_temp): 276 | shutil.rmtree(input_temp) 277 | if os.path.exists(output_temp): 278 | shutil.rmtree(output_temp) 279 | os.makedirs(input_temp) 280 | os.makedirs(output_temp) 281 | 282 | # load docker and create a new folder to save segmentation results 283 | teamname = docker.split('.')[0].lower() 284 | print('teamname docker: ', docker) 285 | os.system('docker image load -i {}'.format(join(docker_path, docker))) 286 | team_outpath = join(save_path, teamname) 287 | if os.path.exists(team_outpath): 288 | shutil.rmtree(team_outpath) 289 | os.makedirs(team_outpath) 290 | os.system(f'chmod -R 777 ./* >/dev/null 2>&1') # ignore output warnings/errors of this command with >/dev/null 2>&1 291 | 292 | # Evaluation Metrics 293 | metric = OrderedDict() 294 | metric['CaseName'] = [] 295 | # 5 Metrics 296 | metric['TotalRunningTime'] = [] 297 | metric['RunningTime_1'] = [] 298 | metric['RunningTime_2'] = [] 299 | metric['RunningTime_3'] = [] 300 | metric['RunningTime_4'] = [] 301 | metric['RunningTime_5'] = [] 302 | metric['RunningTime_6'] = [] 303 | metric['DSC_AUC'] = [] 304 | metric['NSD_AUC'] = [] 305 | metric['DSC_Final'] = [] 306 | metric['NSD_Final'] = [] 307 | metric['DSC_1'] = [] 308 | metric['DSC_2'] = [] 309 | metric['DSC_3'] = [] 310 | metric['DSC_4'] = [] 311 | metric['DSC_5'] = [] 312 | metric['DSC_6'] = [] 313 | metric['NSD_1'] = [] 314 | metric['NSD_2'] = [] 315 | metric['NSD_3'] = [] 316 | metric['NSD_4'] = [] 317 | metric['NSD_5'] = [] 318 | metric['NSD_6'] = [] 319 | metric['num_class'] = [] 320 | metric['runtime_upperbound'] = [] 321 | n_clicks = 5 322 | time_warning = False 323 | 324 | # To obtain the running time for each case, testing cases are inferred one-by-one 325 | for case in tqdm(test_cases): 326 | 327 | metric_temp = {} 328 | real_running_time = 0 329 | dscs = [] 330 | nsds = [] 331 | all_segs = [] 332 | no_bbox = False 333 | 334 | # copy input image to accumulate clicks in its dict 335 | shutil.copy(join(test_img_path, case), input_temp) 336 | if validation_gts_path is None: # for training images 337 | gts = patched_np_load(join(input_temp, case), allow_pickle=True)['gts'] 338 | else: # for validation or test images --> gts are in separate files to avoid label leakage during the course of the challenge 339 | gts = patched_np_load(join(validation_gts_path, case), allow_pickle=True)['gts'] 340 | 341 | unique_gts = np.sort(pd.unique(gts.ravel())) 342 | num_classes = len(unique_gts) - 1 343 | metric_temp['num_class'] = num_classes 344 | metric_temp['runtime_upperbound'] = num_classes * 90 345 | 346 | 347 | # foreground and background clicks for each class 348 | clicks_cls = [{'fg': [], 'bg': []} for _ in unique_gts[1:]] # skip background class 0 349 | clicks_order = [[] for _ in unique_gts[1:]] 350 | if "boxes" in patched_np_load(join(input_temp, case), allow_pickle=True).keys(): 351 | boxes = patched_np_load(join(input_temp, case), allow_pickle=True)['boxes'] 352 | 353 | 354 | for it in range(n_clicks + 1): # + 1 due to bbox pred at iteration 0 355 | if it == 0: 356 | if "boxes" not in patched_np_load(join(input_temp, case), allow_pickle=True).keys(): 357 | if verbose: 358 | print(f'This sample does not use a Bounding Box for the initial iteration {it}') 359 | no_bbox = True 360 | metric_temp["RunningTime_1"] = 0 361 | metric_temp["DSC_1"] = 0 362 | metric_temp["NSD_1"] = 0 363 | dscs.append(0) 364 | nsds.append(0) 365 | continue 366 | if verbose: 367 | print(f'Using Bounding Box for iteration {it}') 368 | else: 369 | if verbose: 370 | print(f'Using Clicks for iteration {it}') 371 | if os.path.isfile(join(output_temp, case)): 372 | segs = patched_np_load(join(output_temp, case), allow_pickle=True)['segs'].astype(np.uint8) # previous prediction 373 | else: 374 | segs = np.zeros_like(gts).astype(np.uint8) # in case the bbox prediction did not produce a result 375 | 376 | # Refinement clicks 377 | for ind, cls in enumerate(sorted(unique_gts[1:])): 378 | if cls == 0: 379 | continue # skip background 380 | 381 | segs_cls = (segs == cls).astype(np.uint8) 382 | gts_cls = (gts == cls).astype(np.uint8) 383 | 384 | # Compute error mask 385 | error_mask = (segs_cls != gts_cls).astype(np.uint8) 386 | if np.sum(error_mask) > 0: 387 | errors = cc3d.connected_components(error_mask, connectivity=26) # 26 for 3D connectivity 388 | 389 | # Calculate the sizes of connected error components 390 | component_sizes = np.bincount(errors.flat) 391 | 392 | # Ignore non-error regions 393 | component_sizes[0] = 0 394 | 395 | # Find the largest error component 396 | largest_component_error = np.argmax(component_sizes) 397 | 398 | # Find the voxel coordinates of the largest error component 399 | largest_component = (errors == largest_component_error) 400 | 401 | edt = compute_edt(largest_component) 402 | edt *= largest_component # make sure correct voxels have a distance of 0 403 | if np.sum(edt) == 0: # no valid voxels to sample 404 | if verbose: 405 | print("Error is extremely small --> Sampling uniformly instead of using EDT") 406 | edt = largest_component # in case EDT is empty (due to artifacts in resizing, simply sample a random voxel from the component), happens only for extremely small errors 407 | 408 | center = sample_coord(edt) 409 | 410 | if gts_cls[center] == 0: # oversegmentation -> place background click 411 | assert segs_cls[center] == 1 412 | clicks_cls[ind]['bg'].append(list(center)) 413 | clicks_order[ind].append('bg') 414 | else: # undersegmentation -> place foreground click 415 | assert segs_cls[center] == 0 416 | clicks_cls[ind]['fg'].append(list(center)) 417 | clicks_order[ind].append('fg') 418 | 419 | assert largest_component[center] # click within error 420 | 421 | if verbose: 422 | print(f"Class {cls}: Largest error component center is at {center}") 423 | else: 424 | clicks_order[ind].append(None) 425 | if verbose: 426 | print(f"Class {cls}: No error connected components found. Prediction is perfect! No clicks were added.") 427 | 428 | # update model input with new click 429 | input_img = patched_np_load(join(input_temp, case), allow_pickle=True) 430 | 431 | if validation_gts_path is None: 432 | if no_bbox: 433 | np.savez_compressed( 434 | join(input_temp, case), 435 | imgs=input_img['imgs'], 436 | gts=input_img['gts'], # only for training images 437 | spacing=input_img['spacing'], 438 | clicks=clicks_cls, 439 | clicks_order=clicks_order, 440 | prev_pred=segs, 441 | ) 442 | else: 443 | np.savez_compressed( 444 | join(input_temp, case), 445 | imgs=input_img['imgs'], 446 | gts=input_img['gts'], # only for training images 447 | spacing=input_img['spacing'], 448 | clicks=clicks_cls, 449 | clicks_order=clicks_order, 450 | prev_pred=segs, 451 | boxes=boxes, 452 | ) 453 | else: 454 | if no_bbox: 455 | np.savez_compressed( 456 | join(input_temp, case), 457 | imgs=input_img['imgs'], 458 | spacing=input_img['spacing'], 459 | clicks=clicks_cls, 460 | clicks_order=clicks_order, 461 | prev_pred=segs, 462 | ) 463 | else: 464 | np.savez_compressed( 465 | join(input_temp, case), 466 | imgs=input_img['imgs'], 467 | spacing=input_img['spacing'], 468 | clicks=clicks_cls, 469 | clicks_order=clicks_order, 470 | prev_pred=segs, 471 | boxes=boxes, 472 | ) 473 | 474 | # Model inference on the current input 475 | if torch.cuda.is_available(): # GPU available 476 | cmd = 'docker container run --gpus "device=0" -m 32G --name {} --rm -v $PWD/inputs/:/workspace/inputs/ -v $PWD/outputs/:/workspace/outputs/ {}:latest /bin/bash -c "sh predict.sh" '.format(teamname.replace('/', '_'), teamname.split('_')[0]) 477 | else: 478 | cmd = 'docker container run -m 32G --name {} --rm -v $PWD/inputs/:/workspace/inputs/ -v $PWD/outputs/:/workspace/outputs/ {}:latest /bin/bash -c "sh predict.sh" '.format(teamname.replace('/', '_'), teamname.split('_')[0]) 479 | if verbose: 480 | print(teamname, ' docker command:', cmd, '\n', 'testing image name:', case) 481 | start_time = time.time() 482 | os.system(cmd) 483 | infer_time = time.time() - start_time 484 | real_running_time += infer_time # only add the inference time without the click generation time 485 | print(f"{case} finished! Inference time: {infer_time}") 486 | metric_temp[f"RunningTime_{it + 1}"] = infer_time 487 | 488 | if not os.path.isfile(join(output_temp, case)): 489 | print(f"[WARNING] Failed / Skipped prediction for iteration {it}! Setting prediction to zeros...") 490 | segs = np.zeros_like(gts).astype(np.uint8) 491 | else: 492 | segs = patched_np_load(join(output_temp, case), allow_pickle=True)['segs'] 493 | all_segs.append(segs.astype(np.uint8)) 494 | 495 | dsc = compute_multi_class_dsc(gts, segs) 496 | # compute nsd 497 | if dsc > 0.2: 498 | # only compute nsd when dice > 0.2 because NSD is also low when dice is too low 499 | nsd = compute_multi_class_nsd(gts, segs, patched_np_load(join(input_temp, case), allow_pickle=True)['spacing']) 500 | else: 501 | nsd = 0.0 # Assume model performs poor on this sample 502 | dscs.append(dsc) 503 | nsds.append(nsd) 504 | metric_temp[f'DSC_{it + 1}'] = dsc 505 | metric_temp[f'NSD_{it + 1}'] = nsd 506 | print('Dice', dsc, 'NSD', nsd) 507 | seg_name = case 508 | 509 | 510 | # Copy temp prediction to the final folder 511 | try: 512 | shutil.copy(join(output_temp, seg_name), join(team_outpath, seg_name)) 513 | segs = patched_np_load(join(team_outpath, seg_name), allow_pickle=True)['segs'] 514 | np.savez_compressed( 515 | join(team_outpath, seg_name), 516 | segs=segs, 517 | all_segs=all_segs, # store all intermediate predictions 518 | ) 519 | except: 520 | print(f"{join(output_temp, seg_name)}, {join(team_outpath, seg_name)}") 521 | if os.path.exists(join(team_outpath, seg_name)): 522 | os.remove(team_outpath, seg_name) # clean up cached files if model has failed 523 | print("Final prediction could not be copied!") 524 | 525 | 526 | if real_running_time > 90 * (len(unique_gts) - 1): 527 | print("[WARNING] Your model seems to take more than 90 seconds per class during inference! The final test set will have a time constraint of 90s per class --> Make sure to optimize your approach!") 528 | time_warning = True 529 | # Compute interactive metrics 530 | dsc_auc = integrate.cumulative_trapezoid(np.array(dscs[-n_clicks:]), np.arange(n_clicks))[-1] # AUC is only over the point prompts since the bbox prompt is optional 531 | nsd_auc = integrate.cumulative_trapezoid(np.array(nsds[-n_clicks:]), np.arange(n_clicks))[-1] 532 | dsc_final = dscs[-1] 533 | nsd_final = nsds[-1] 534 | if os.path.exists(join(team_outpath, seg_name)): # add to csv only if final prediction is successful 535 | for k, v in metric_temp.items(): 536 | metric[k].append(v) 537 | metric['CaseName'].append(case) 538 | metric['TotalRunningTime'].append(real_running_time) 539 | metric['DSC_AUC'].append(dsc_auc) 540 | metric['NSD_AUC'].append(nsd_auc) 541 | metric['DSC_Final'].append(dsc_final) 542 | metric['NSD_Final'].append(nsd_final) 543 | os.remove(join(input_temp, case)) 544 | 545 | metric_df = pd.DataFrame(metric) 546 | metric_df.to_csv(join(team_outpath, teamname + '_metrics.csv'), index=False) 547 | 548 | # Clean up for next docker 549 | torch.cuda.empty_cache() 550 | os.system("docker rmi {}:latest".format(teamname.split('_')[0])) 551 | shutil.rmtree(input_temp) 552 | shutil.rmtree(output_temp) 553 | if time_warning: # repeat warning at the end as well 554 | print("[WARNING] Your model seems to take more than 90 seconds per class during inference for some images! The final test set will have a time constraint of 90s per class --> Make sure to optimize your approach!") 555 | except Exception as e: 556 | print(e) 557 | traceback.print_exc() 558 | print(f"Error processing {case} with docker {docker}. Skipping this docker.") 559 | --------------------------------------------------------------------------------