Multi-view SA-LA Net: A framework for simultaneous segmentation of RV on multi-view cardiac MR Images

TL;DR

This paper introduces a multi-view SA-LA neural network framework that simultaneously segments the right ventricle in multi-view cardiac MR images, leveraging multi-encoder/decoder architecture, spatial priors, and deep supervision for improved accuracy.

Contribution

The novel multi-view SA-LA model combines features from short-axis and long-axis images in a multi-encoder, multi-decoder U-Net architecture, enhancing RV segmentation performance.

Findings

Achieved 91% Dice score on challenge dataset

Demonstrated strong generalization to unseen RV pathologies

Outperformed existing methods in multi-view cardiac MR segmentation

Abstract

We proposed a multi-view SA-LA model for simultaneous segmentation of RV on the short-axis (SA) and long-axis (LA) cardiac MR images. The multi-view SA-LA model is a multi-encoder, multi-decoder U-Net architecture based on the U-Net model. One encoder-decoder pair segments the RV on SA images and the other pair on LA images. Multi-view SA-LA model assembles an extremely rich set of synergistic features, at the root of the encoder branch, by combining feature maps learned from matched SA and LA cardiac MR images. Segmentation performance is further enhanced by: (1) incorporating spatial context of LV as a prior and (2) performing deep supervision in the last three layers of the decoder branch. Multi-view SA-LA model was extensively evaluated on the MICCAI 2021 Multi- Disease, Multi-View, and Multi- Centre RV Segmentation Challenge dataset (M&Ms-2021). M&Ms-2021 dataset consists of multi-phase, multi-view cardiac MR images of 360 subjects acquired at four clinical centers with three different vendors. On the challenge cohort (160 subjects), the proposed multi-view SA-LA model achieved a Dice Score of 91% and Hausdorff distance of 11.2 mm on short-axis images and a Dice Score of 89.6% and Hausdorff distance of 8.1 mm on long-axis images. Moreover, multi-view SA-LA model exhibited strong generalization to unseen RV related pathologies including Dilated Right Ventricle (DSC: SA 91.41%, LA 89.63%) and Tricuspidal Regurgitation (DSC: SA 91.40%, LA 90.40%) with low variance (std_DSC: SA <5%, LA<6%).