README.md

---
license: mit
pipeline_tag: image-segmentation
tags:
- medical
- biology
---
# CT Heart Segmentation Model

**Model Information:**
- **Architecture:** U-Net
- **Task:** Binary segmentation of heart in CT scans
- **Dataset:** [Heart CT Dataset](https://www.kaggle.com/datasets/nikhilroxtomar/ct-heart-segmentation)
- **Input Size:** 224×224 RGB images

**Performance Metrics:**
- **Best Dice Score:** 0.9479
- **Best IoU Score:** 0.9014

**Usage:**
```python
from shifaa.vision import VisionModelFactory

model = VisionModelFactory.create_model(
    model_type="segmentation",
    model_name="CT_Heart"
)

results = model.run("heart_ct.png", show_image=True)
image = results["image"]
mask = results["predicted_mask"]
```

**Sample Results:**
![Sample Results](./CT_H_seg.png)


**Architecture Details:**
- Encoder: 4 downsampling blocks (Conv → BatchNorm → ReLU → Dropout)
- Bottleneck: Deepest convolutional block
- Decoder: 4 upsampling blocks with skip connections
- Output: 1 channel with sigmoid activation

**Preprocessing:**
- Random horizontal flip
- Random rotation ±15°
- Random brightness & contrast adjustment
- Normalize and convert to tensor

**Training Details:**
- **Loss Function:** Combined Dice Loss + BCE Loss
- **Optimizer:** Adam (lr=0.001, weight_decay=1e-5)
- **Batch Size:** 8
- **Epochs:** 100 (with early stopping)

---