Shifaa-Heart-CT-UNet

CT Heart Segmentation Model

Model Information:

• Architecture: U-Net
• Task: Binary segmentation of heart in CT scans
• Dataset: Heart CT Dataset
• Input Size: 224×224 RGB images

Performance Metrics:

• Best Dice Score: 0.9479
• Best IoU Score: 0.9014

Usage:

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:

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)

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