# Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transition

arXiv: https://arxiv.org/abs/2109.02171 · HTML: https://mlsanity.com/paper/2109.02171/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 5 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge](https://mlsanity.com/paper/2006.12434/) (2020)
- [Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors](https://mlsanity.com/paper/1906.07347/) (2019)
- [Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images](https://mlsanity.com/paper/1907.09983/) (2019)
- [Omega-Net (Omega-Net): Fully Automatic, Multi-View Cardiac MR Detection, Orientation, and Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1711.01094/) (2017)

## Built on by (most cited first, 9 of 9)

- [TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformers](https://mlsanity.com/paper/2203.10726/) (2022)
- [Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation](https://mlsanity.com/paper/2302.03537/) (2023)
- [GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentation](https://mlsanity.com/paper/2309.11144/) (2023)
- [CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation](https://mlsanity.com/paper/2406.05786/) (2024)
- [EchoONE: Segmenting Multiple echocardiography Planes in One Model](https://mlsanity.com/paper/2412.02993/) (2024)
- [Crop and Couple: cardiac image segmentation using interlinked specialist networks](https://mlsanity.com/paper/2402.09156/) (2024)
- [How good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluation](https://mlsanity.com/paper/2408.06358/) (2024)
- [Compositional Segmentation of Cardiac Images Leveraging Metadata](https://mlsanity.com/paper/2410.23130/) (2024)
- [Multi-view Cardiac Image Segmentation via Trans-Dimensional Priors](https://mlsanity.com/paper/2404.16708/) (2024)

## Abstract (arXiv)

Right ventricular (RV) segmentation from magnetic resonance imaging (MRI) is a crucial step for cardiac morphology and function analysis. However, automatic RV segmentation from MRI is still challenging, mainly due to the heterogeneous intensity, the complex variable shapes, and the unclear RV boundary. Moreover, current methods for the RV segmentation tend to suffer from performance degradation at the basal and apical slices of MRI. In this work, we propose an automatic RV segmentation framework, where the information from long-axis (LA) views is utilized to assist the segmentation of short-axis (SA) views via information transition. Specifically, we employed the transformed segmentation from LA views as a prior information, to extract the ROI from SA views for better segmentation. The information transition aims to remove the surrounding ambiguous regions in the SA views. %, such as the tricuspid valve regions. We tested our model on a public dataset with 360 multi-center, multi-vendor and multi-disease subjects that consist of both LA and SA MRIs. Our experimental results show that including LA views can be effective to improve the accuracy of the SA segmentation. Our model is publicly available at https://github.com/NanYoMy/MMs-2.