# Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images

## TL;DR

This paper introduces a novel multi-view shape prior learning approach for robust cardiac MR segmentation, combining 2D networks with spatial context from multiple views to improve accuracy and data efficiency.

## Contribution

It proposes a new method that learns anatomical shape priors across multiple views to enhance 2D segmentation of cardiac MR images, outperforming existing models.

## Key findings

- Achieves more accurate myocardium segmentation than baseline models.
- Reduces Hausdorff distance significantly with limited training data.
- Outperforms 2D and 3D U-Net models in robustness and efficiency.

## Abstract

Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart. Inspired by how experienced clinicians assess the cardiac morphology and function across multiple standard views (i.e. long- and short-axis views), we propose a novel approach which learns anatomical shape priors across different 2D standard views and leverages these priors to segment the left ventricular (LV) myocardium from short-axis MR image stacks. The proposed segmentation method has the advantage of being a 2D network but at the same time incorporates spatial context from multiple, complementary views that span a 3D space. Our method achieves accurate and robust segmentation of the myocardium across different short-axis slices (from apex to base), outperforming baseline models (e.g. 2D U-Net, 3D U-Net) while achieving higher data efficiency. Compared to the 2D U-Net, the proposed method reduces the mean Hausdorff distance (mm) from 3.24 to 2.49 on the apical slices, from 2.34 to 2.09 on the middle slices and from 3.62 to 2.76 on the basal slices on the test set, when only 10% of the training data was used.