ACDC Segmenter

One of the foundational baseline segmentation networks submitted to the 2017 Automated Cardiac Diagnosis Challenge (ACDC), comparing 2D and 3D convolutional network designs for segmenting the left ventricle cavity, myocardium, and right ventricle cavity from short-axis cine cardiac MRI at end-diastole and end-systole. The accompanying study systematically explored the tradeoffs between 2D and 3D convolutions for this task, finding that, due to the highly anisotropic voxel spacing typical of clinical cine cardiac MRI, 2D networks that treat each slice independently can match or exceed 3D networks while being far cheaper to train. The public code and pretrained weights for the best-performing configuration have served as a widely used, simple baseline for later cardiac MRI segmentation research (including for automatically deriving ventricular volumes and ejection fraction).