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-# CT-Heart-Segmentation
-
-## **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.  
-
-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**.  
-
----
-
-## **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.  
-
-### **Kaggle link for Dataset:** https://www.kaggle.com/datasets/nikhilroxtomar/ct-heart-segmentation
-
-### **Preprocessing Steps:**
-✅ Resizing images to a fixed resolution  
-✅ Normalizing pixel values for stable training  
-
----
-
-## **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.  
-
-### **Augmentations Applied:**
-✅ **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  
-✅ **RGBShift** → Shifts the red, green, and blue channels to create different color variations  
-✅ **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  
-✅ **ChannelShuffle** → Randomly shuffles color channels to increase diversity in training samples  
-
----
-
-## **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.  
-
-### **U-Net Architecture Highlights:**
-🔹 **Encoder** (Contracting Path) → Captures spatial features using convolutional layers  
-🔹 **Bottleneck** → Connects the encoder and decoder with high-level feature maps  
-🔹 **Decoder** (Expanding Path) → Recovers spatial resolution for precise segmentation  
-🔹 **Skip Connections** → Retains fine-grained details by merging encoder features  
-
----
-
-## **Training Process **  
-The model was trained using TensorFlow/Keras with the following setup:  
-✅ Loss Function → Dice Loss  
-✅ Optimizer → Adam optimizer for efficient learning  
-✅ Learning Rate Scheduling → Reduces learning rate on plateaus  
-✅ Evaluation Metrics → Dice Coefficient, IoU, Accuracy   
-
-
-### Examples of CT Segmentation
-Below are results of Unet segmentation:
-
-the image from validation set is structred as [Image - True mask - Predicted mask] 
-![Heart Segmentation Visualization](results/CT1.png)  
-![Heart Segmentation Visualization](results/CT3.png)
-
-the image from test set is structred as [Image - Predicted mask] 
-![Heart Segmentation Visualization](results/CT7.png)  
-![Heart Segmentation Visualization](results/CT8.png)
+# CT-Heart-Segmentation
+
+## **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.  
+
+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**.  
+
+---
+
+## **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.  
+
+### **Kaggle link for Dataset:** https://www.kaggle.com/datasets/nikhilroxtomar/ct-heart-segmentation
+
+### **Preprocessing Steps:**
+✅ Resizing images to a fixed resolution  
+✅ Normalizing pixel values for stable training  
+
+---
+
+## **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.  
+
+### **Augmentations Applied:**
+✅ **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  
+✅ **RGBShift** → Shifts the red, green, and blue channels to create different color variations  
+✅ **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  
+✅ **ChannelShuffle** → Randomly shuffles color channels to increase diversity in training samples  
+
+---
+
+## **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.  
+
+### **U-Net Architecture Highlights:**
+🔹 **Encoder** (Contracting Path) → Captures spatial features using convolutional layers  
+🔹 **Bottleneck** → Connects the encoder and decoder with high-level feature maps  
+🔹 **Decoder** (Expanding Path) → Recovers spatial resolution for precise segmentation  
+🔹 **Skip Connections** → Retains fine-grained details by merging encoder features  
+
+---
+
+## **Training Process **  
+The model was trained using TensorFlow/Keras with the following setup:  
+✅ Loss Function → Dice Loss  
+✅ Optimizer → Adam optimizer for efficient learning  
+✅ Learning Rate Scheduling → Reduces learning rate on plateaus  
+✅ Evaluation Metrics → Dice Coefficient, IoU, Accuracy   
+
+