# GI Tract Image Segmentation

## Overview

This project implements a **U-Net deep learning model** for **medical image segmentation**, specifically targeting segmentation of the gastrointestinal (GI) tract from medical scans. The pipeline includes custom data preprocessing, a modular training process, and exportable predictions in **Run-Length Encoding (RLE)** format.

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## Key Features

- **Data Pipeline**:

  - Custom data generator to handle large datasets with RLE-encoded masks.

  - Dynamic resizing of images and masks to a configurable target size.

  - Flexible test mode for visualizing individual predictions and ground truths.

- **Model Architecture**:

  - Based on **TransUNet**, combining convolutional layers with transformer-based features for superior performance.

  - Option to load pre-trained weights for transfer learning or train from scratch.

- **Evaluation**:

  - Metrics include **Dice coefficient**, **accuracy**, and **visual analysis** of predictions.

  - Visualization overlays for ground truths and predictions on original images.

- **Export**:

  - Saves predictions in RLE format compatible with Kaggle competitions or downstream pipelines.

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## Requirements

To set up and run the project, ensure the following dependencies are installed:

- TensorFlow 2.8+

- Keras

- NumPy

- Pandas

- OpenCV

- Matplotlib

- Scikit-learn

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## Usage

### **1. Running the Pipeline**

Run the main script to train, evaluate, or export predictions:

python GI-Tract-Image-Segmentation.py

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### **2. Training the Model**

If training from scratch:

- Automatically splits the data into training, validation, and test sets.

- Implements early stopping, learning rate scheduling, and model checkpointing.

- Saves the best model weights to the `output/` directory.

### **3. Evaluating the Model**

During evaluation, the script:

- Computes **Dice coefficient** and loss for each test sample.

- Visualizes predictions and overlays with ground truths.

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### **Data Pipeline**

The project includes a highly modular pipeline:

- **Custom Generator**:

  - Decodes RLE masks into binary masks dynamically.

  - Handles resizing, augmentation, and batch generation.

- **Training Pipeline**:

  - Modularized for scalability and customization.

  - Includes checkpoints and CSV logging of training metrics.

## Model Architecture

- **Base Model**: TransUNet

  - Combines transformer layers for long-range dependency capture with CNNs for spatial feature extraction.

- **Custom Modifications**:

  - Configurable input size.

  - Optional pre-trained weights for transfer learning.