Right ventricular segmentation from short- and long-axis MRIs via information transition
Right ventricular segmentation from short- and long-axis MRIs via information transition
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. 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.
Information transition, RV segmentation, Short-axis and long-axis MRI
259-267
Li, Lei
2da88502-0bd8-4e6b-8f7d-0c01a48b399e
Ding, Wangbin
ce6c8e72-9208-49fc-b79d-780d4293bc15
Huang, Liqin
4565d61e-d3eb-4ffd-97af-731ae41e5335
Zhuang, Xiahai
c58e977b-e70e-4b37-9acd-b7f8070d98a8
14 January 2022
Li, Lei
2da88502-0bd8-4e6b-8f7d-0c01a48b399e
Ding, Wangbin
ce6c8e72-9208-49fc-b79d-780d4293bc15
Huang, Liqin
4565d61e-d3eb-4ffd-97af-731ae41e5335
Zhuang, Xiahai
c58e977b-e70e-4b37-9acd-b7f8070d98a8
Li, Lei, Ding, Wangbin, Huang, Liqin and Zhuang, Xiahai
(2022)
Right ventricular segmentation from short- and long-axis MRIs via information transition.
Puyol Antón, Esther, Young, Alistair, Suinesiaputra, Avan, Pop, Mihaela, Martín-Isla, Carlos, Sermesant, Maxime, Camara, Oscar and Lekadir, Karim
(eds.)
In Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. STACOM 2021, Held in Conjunction with MICCAI 2021, Revised Selected Papers.
vol. 13131,
Springer Cham.
.
(doi:10.1007/978-3-030-93722-5_28).
Record type:
Conference or Workshop Item
(Paper)
Abstract
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. 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.
This record has no associated files available for download.
More information
Published date: 14 January 2022
Venue - Dates:
12th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2021 held in conjunction with MICCAI 2021, , Strasbourg, France, 2021-09-27 - 2021-09-27
Keywords:
Information transition, RV segmentation, Short-axis and long-axis MRI
Identifiers
Local EPrints ID: 489143
URI: http://eprints.soton.ac.uk/id/eprint/489143
ISSN: 0302-9743
PURE UUID: ab9e2145-550f-4bfb-8030-8e81fc4bdb95
Catalogue record
Date deposited: 15 Apr 2024 16:58
Last modified: 16 Apr 2024 02:09
Export record
Altmetrics
Contributors
Author:
Lei Li
Author:
Wangbin Ding
Author:
Liqin Huang
Author:
Xiahai Zhuang
Editor:
Esther Puyol Antón
Editor:
Alistair Young
Editor:
Avan Suinesiaputra
Editor:
Mihaela Pop
Editor:
Carlos Martín-Isla
Editor:
Maxime Sermesant
Editor:
Oscar Camara
Editor:
Karim Lekadir
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics