Cardiac Segmentation & Heart Disease Diagnosis (ACDC 3D nnU-Net)

An end-to-end medical AI pipeline for automated 3D cardiac segmentation, clinical biomarker derivation (Ejection Fraction, Ventricular Volumes, Myocardial Mass), and 5-class cardiac disease diagnosis from short-axis cine cardiac MRI scans.

• GitHub Repository: Makifkaradag/cardiac-segmentation-diagnosis
• Framework: nnU-Net v2 (3d_fullres) & Random Forest Classifier
• Benchmark Dataset: Automated Cardiac Diagnosis Challenge (ACDC, MICCAI)

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Model Summary

• Task 1 (Segmentation): Full 3D segmentation of three cardiac structures:
  • Label 1: Right Ventricle Cavity (RV)
  • Label 2: Left Ventricle Myocardium (MYO)
  • Label 3: Left Ventricle Cavity (LV)
• Task 2 (Clinical Biomarker Extraction): Automatically derives Left/Right Ventricular End-Diastolic Volume (EDV), End-Systolic Volume (ESV), Ejection Fraction (EF %), and Myocardial Mass (g).
• Task 3 (Diagnosis Classification): 5-class pathology classification:
  • NOR: Normal cardiac function
  • MINF: Previous Myocardial Infarction
  • DCM: Dilated Cardiomyopathy
  • HCM: Hypertrophic Cardiomyopathy
  • RV: Abnormal Right Ventricle

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Benchmark Performance & Evaluation

Evaluated on 100 ACDC training patients (5-fold cross-validation) and 50 holdout cases.

1. 3D Segmentation Quality (Holdout Set)

2. Clinical Biomarker Agreement (Ground Truth vs. Predictions)

3. Diagnosis Classification (5-Fold Stratified Cross-Validation)

• Overall Accuracy: 0.880 (88.0%)

• End-to-End Holdout Accuracy: 0.860 (86.0%) evaluated purely on predicted masks without human ground truth.

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How to Download and Use from Hugging Face

1. Download Model Weights via Python

from huggingface_hub import snapshot_download

# Download model repository
model_path = snapshot_download(repo_id="Makifkaradag/cardiac-segmentation-diagnosis")
print("Model files downloaded to:", model_path)