Introduction

Winner of the 2017 MICCAI ACDC (Automatic Cardiac Diagnostic Challenge). The method uses an ensemble of 2 CNNs for segmentation and an ensemble of MLPs and a random forest for diagnostic.

Segmentation

As shown in Fig.2, they use an ensemble of a 2D and a 3D unet. These are usual unets but with a feature map combination on the deconvolution side.

Classification

Given the segmentation map obtained by their method, they extract the following cardiac features:

These features are then used to train 50 MLPs and a random forest of 1000 trees. The output of these models are then combined for prediction.

Results

The got very good results with only 4 erroneous diagnosis

and the best overall segmentation accuracy

Code

Segmentation code is available here