---
license: mit
tags:
  - medical-imaging
  - cardiac-mri
  - segmentation
  - attention-unet
  - pytorch
datasets:
  - ACDC
pipeline_tag: image-segmentation
---

# ACDC Heart Segmentation — Attention U-Net Ensemble

A 5-fold cross-validated **Attention U-Net** ensemble trained on the [ACDC cardiac MRI dataset](https://www.creatis.insa-lyon.fr/Challenge/acdc/) for multi-class segmentation of cardiac structures.

## Model Description

This model segments cardiac MRI short-axis slices into 4 classes:
- **Class 0**: Background
- **Class 1**: Right Ventricle (RV)
- **Class 2**: Myocardium (LVM)
- **Class 3**: Left Ventricle (LVC)

### Architecture
- **Base**: U-Net with Attention Gates
- **Input**: Single-channel grayscale MRI (256x256)
- **Output**: 4-class segmentation map
- **Training**: 5-fold cross-validation on the ACDC training set

## Usage

```python
import torch
from model import AttentionUNet

model = AttentionUNet(img_ch=1, output_ch=4)
state_dict = torch.load("fold_1_model.pth", map_location="cpu", weights_only=False)
if 'model_state_dict' in state_dict:
    state_dict = state_dict['model_state_dict']
model.load_state_dict(state_dict)
model.eval()

# Input: [batch, 1, 256, 256] normalized to mean=0.5, std=0.5
img_tensor = torch.randn(1, 1, 256, 256)
with torch.no_grad():
    output = model(img_tensor)  # [batch, 4, 256, 256]
    pred = torch.argmax(output, dim=1)  # [batch, 256, 256]
```

## Files

| File | Description |
|------|-------------|
| `model.py` | Model architecture (AttentionUNet) |
| `fold_1_model.pth` - `fold_5_model.pth` | Trained weights for each CV fold |

## Training Details

- **Dataset**: ACDC (Automated Cardiac Diagnosis Challenge)
- **Optimizer**: Adam
- **Loss**: Cross-Entropy + Dice Loss
- **Image Size**: 256x256
- **Normalization**: (pixel - 0.5) / 0.5