└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # U-Net for Image Segmentation 2 | 3 | This repository provides an easy-to-understand guide to the U-Net architecture, a powerful tool for image segmentation tasks. U-Net is widely used in biomedical imaging and other fields where precise segmentation of objects within images is crucial. 4 | ![Image](https://github.com/user-attachments/assets/6b7a0486-2977-46d0-8787-eb899cf2b4d3) 5 | 6 | ## Table of Contents 7 | 8 | 1. [Introduction to Image Segmentation](#introduction) 9 | 2. [Understanding U-Net Architecture](#understanding-u-net-architecture) 10 | 3. [Key Components of U-Net](#key-components-of-u-net) 11 | 4. [How U-Net Works](#how-u-net-works) 12 | 5. [Results](#results) 13 | 6. [Visualizing Results](#visualizing-results) 14 | 15 | ## Introduction to Image Segmentation 16 | 17 | Image segmentation is the process of dividing an image into segments or regions that correspond to different objects or parts of objects. Unlike classification, which assigns a label to an entire image, segmentation assigns a label to every pixel in the image. This is particularly useful in medical imaging, where identifying specific tissues or anomalies is essential. 18 | 19 | ![Image](https://github.com/user-attachments/assets/4f3c76f0-dddd-448f-b407-edc18353577d) 20 | 21 | ## Understanding U-Net Architecture 22 | 23 | U-Net is named for its U-shaped architecture, which consists of a contracting path (encoder) and an expansive path (decoder). The architecture allows the network to capture context and spatial information effectively. 24 | 25 | ![Image](https://github.com/user-attachments/assets/61eac877-1993-4ec2-93ac-0593e4262325) 26 | 27 | ### Why U-Net? 28 | 29 | - **Precision**: U-Net is designed to work with very few training images and still yield precise segmentation. 30 | - **Efficiency**: The use of skip connections helps in retaining spatial information, making the model efficient in localizing features. 31 | 32 | ## Key Components of U-Net 33 | 34 | ### 1. Contracting Path (Encoder) 35 | 36 | The contracting path is responsible for capturing the context in the image. It consists of repeated applications of convolutions, followed by a max pooling operation. This process reduces the spatial dimensions while increasing the depth of the feature maps. 37 | 38 | - **Example**: Imagine shrinking an image to focus on important features, like edges or textures, while discarding less important details. 39 | 40 | ### 2. Expansive Path (Decoder) 41 | 42 | The expansive path is responsible for enabling precise localization using transposed convolutions. It combines features from the contracting path via skip connections to reconstruct the segmentation map. 43 | 44 | - **Example**: Think of zooming back into the image but with added knowledge of what features are important, allowing you to accurately label each pixel. 45 | 46 | ### 3. Skip Connections 47 | 48 | Skip connections are a crucial part of U-Net. They connect layers in the contracting path to the expansive path, allowing the network to recover spatial information lost during downsampling. 49 | 50 | - **Example**: If you're drawing a detailed map, you might refer back to a rough sketch to ensure you don't miss any important landmarks. 51 | 52 | ## How U-Net Works 53 | 54 | 1. **Input Image**: The network takes an input image, such as a medical scan or a photograph. 55 | 2. **Contracting Path**: The image is passed through several layers, each extracting features and reducing the spatial dimensions. 56 | 3. **Bottleneck**: At the deepest layer, the network has a compressed representation of the image. 57 | 4. **Expansive Path**: The network then upsamples this representation, combining it with features from the contracting path to produce a segmentation map. 58 | 5. **Output**: The final output is a segmented image where each pixel is labeled according to its class. 59 | 60 | ### Visual Example 61 | 62 | Imagine you have an image of a cell, and you want to segment the nucleus: 63 | 64 | - **Input**: An image of a cell. 65 | - **Contracting Path**: The network identifies edges and textures, reducing the image to its essential features. 66 | - **Expansive Path**: The network reconstructs the image, using the identified features to accurately label each pixel as part of the nucleus or background. 67 | - **Output**: A segmented image where the nucleus is highlighted. 68 | 69 | ## Results 70 | 71 | | Metric | Value | 72 | |--------------|-------| 73 | | Training Acc | 75% | 74 | | Validation | 73% | 75 | | Test | 72.7% | 76 | 77 | ## Visualizing Results 78 | 79 | ![Image](https://github.com/user-attachments/assets/b3ebb385-07eb-4472-887d-0d9186c17d83) 80 | 81 | ## Conclusion 82 | 83 | U-Net is a powerful tool for image segmentation, particularly in fields where precise localization is crucial. This guide provides a conceptual overview to help you understand the core principles behind U-Net. For implementation details, please refer to the code in the repository. 84 | 85 | 86 | ## Acknowledgments 87 | 88 | - The [U-Net architecture](https://arxiv.org/abs/1505.04597) was originally proposed by Ronneberger et al. (2015). 89 | - The [Oxford-IIIT Pet dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) is used for training and evaluation in the accompanying notebook. 90 | 91 | ## 👤 Author 92 | 93 | For any questions or issues, please open an issue on GitHub: [@Siddharth Mishra](https://github.com/Sid3503) 94 | 95 | --- 96 | 97 |

98 | Made with ❤️ and lots of ☕ 99 |

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