  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%).
