# CT-Heart-Segmentation

# CT-Heart-Segmentation

## **Overview**  

## **Overview**  

This project focuses on **automated heart segmentation** from **CT scans** using deep learning. We applied **data augmentation** techniques using the **Albumentations library** to enhance model generalization and trained a **U-Net model** to segment heart structures accurately.  

This project focuses on **automated heart segmentation** from **CT scans** using deep learning. We applied **data augmentation** techniques using the **Albumentations library** to enhance model generalization and trained a **U-Net model** to segment heart structures accurately.  

Medical image segmentation is crucial in **cardiovascular disease diagnosis**, treatment planning, and surgical interventions. This project aims to provide a robust and efficient deep learning pipeline for **heart segmentation from CT images**.  

Medical image segmentation is crucial in **cardiovascular disease diagnosis**, treatment planning, and surgical interventions. This project aims to provide a robust and efficient deep learning pipeline for **heart segmentation from CT images**.  

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## **Dataset** 

## **Dataset** 

The dataset consists of **CT scan images** with corresponding segmentation masks representing heart structures. The images undergo preprocessing and augmentation before being fed into the segmentation model.  

The dataset consists of **CT scan images** with corresponding segmentation masks representing heart structures. The images undergo preprocessing and augmentation before being fed into the segmentation model.  

### **Kaggle link for Dataset:** https://www.kaggle.com/datasets/nikhilroxtomar/ct-heart-segmentation

### **Kaggle link for Dataset:** https://www.kaggle.com/datasets/nikhilroxtomar/ct-heart-segmentation

### **Preprocessing Steps:**

### **Preprocessing Steps:**

✅ Resizing images to a fixed resolution  

✅ Resizing images to a fixed resolution  

✅ Normalizing pixel values for stable training  

✅ Normalizing pixel values for stable training  

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## **Data Augmentation using Albumentations**  

## **Data Augmentation using Albumentations**  

To improve the model's generalization and performance, we applied **data augmentation** using the **Albumentations** library. This helps to create diverse training samples, reducing overfitting and improving robustness.  

To improve the model's generalization and performance, we applied **data augmentation** using the **Albumentations** library. This helps to create diverse training samples, reducing overfitting and improving robustness.  

### **Augmentations Applied:**

### **Augmentations Applied:**

✅ **RandomBrightnessContrast** → Adjusts brightness and contrast randomly to simulate variations in scanning conditions  

✅ **RandomBrightnessContrast** → Adjusts brightness and contrast randomly to simulate variations in scanning conditions  

✅ **HueSaturationValue** → Modifies the hue, saturation, and value of the image to introduce color variations  

✅ **HueSaturationValue** → Modifies the hue, saturation, and value of the image to introduce color variations  

✅ **RGBShift** → Shifts the red, green, and blue channels to create different color variations  

✅ **RGBShift** → Shifts the red, green, and blue channels to create different color variations  

✅ **RandomGamma** → Randomly adjusts gamma levels to enhance or darken image regions  

✅ **RandomGamma** → Randomly adjusts gamma levels to enhance or darken image regions  

✅ **CLAHE (Contrast Limited Adaptive Histogram Equalization)** → Enhances local contrast to improve visibility of structures  

✅ **CLAHE (Contrast Limited Adaptive Histogram Equalization)** → Enhances local contrast to improve visibility of structures  

✅ **ChannelShuffle** → Randomly shuffles color channels to increase diversity in training samples  

✅ **ChannelShuffle** → Randomly shuffles color channels to increase diversity in training samples  

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## **Model Architecture: U-Net for Segmentation**  

## **Model Architecture: U-Net for Segmentation**  

We implemented a U-Net model, a well-known CNN-based architecture for biomedical image segmentation. U-Net is effective in capturing both local and global spatial information using its encoder-decoder structure with skip connections.  

We implemented a U-Net model, a well-known CNN-based architecture for biomedical image segmentation. U-Net is effective in capturing both local and global spatial information using its encoder-decoder structure with skip connections.  

### **U-Net Architecture Highlights:**

### **U-Net Architecture Highlights:**

🔹 **Encoder** (Contracting Path) → Captures spatial features using convolutional layers  

🔹 **Encoder** (Contracting Path) → Captures spatial features using convolutional layers  

🔹 **Bottleneck** → Connects the encoder and decoder with high-level feature maps  

🔹 **Bottleneck** → Connects the encoder and decoder with high-level feature maps  

🔹 **Decoder** (Expanding Path) → Recovers spatial resolution for precise segmentation  

🔹 **Decoder** (Expanding Path) → Recovers spatial resolution for precise segmentation  

🔹 **Skip Connections** → Retains fine-grained details by merging encoder features  

🔹 **Skip Connections** → Retains fine-grained details by merging encoder features  

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## **Training Process **  

## **Training Process **  

The model was trained using TensorFlow/Keras with the following setup:  

The model was trained using TensorFlow/Keras with the following setup:  

✅ Loss Function → Dice Loss  

✅ Loss Function → Dice Loss  

✅ Optimizer → Adam optimizer for efficient learning  

✅ Optimizer → Adam optimizer for efficient learning  

✅ Learning Rate Scheduling → Reduces learning rate on plateaus  

✅ Learning Rate Scheduling → Reduces learning rate on plateaus  

✅ Evaluation Metrics → Dice Coefficient, IoU, Accuracy   

✅ Evaluation Metrics → Dice Coefficient, IoU, Accuracy