Left and Right Ventricular Segmentation Based on 3D Region-Aware U-Net

Xiaoting Huang, Wenjie Chen, Xueting Liu, Huisi Wu, Zhenkun Wen, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

The cardiac is one of the essential organs, and the segmentation of the left and right ventricular of cardiac is essential in diagnosing various heart diseases. The most popular method for the segmentation of 3D MRI images is the nnUNet. However, the 3D MRI volume of the ventricular contains other organs which interfere with the segmentation of the ventricular. Hence, we proposed a novel region-aware U-Net segmentation method RegUNet for ventricular segmentation. RegUNet improves the ventricular's segmentation performance by first capturing the region of interest (RoI) of the ventricular and then segmenting the ventricular with the captured RoI features, which reduces the segmentation module's difficulty by keeping the cardiac's features and leaving others such that RegUNet can focus on ventricular segmentation. Besides, since the model segments the ventricular with the captured RoI features, it saves the model's computing resources from identifying the background of the volume. Since 3D cardiac MRI volumes scanned by the different devices have diverse statistical characteristics, which causes the model's performance in processing the multi-source cardiac volumes to be unstable. We stabilize the model's performance with a multi-sources feature normalization strategy, which normalizes the feature from a different source with different parameters. We validated the proposed method on the M&MS dataset, a multi-sources 3D MRI cardiac segmentation dataset. Experiments showed that RegUNet's segmentation ability reached the state-of-the-art.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE 35th International Symposium on Computer-Based Medical Systems, CBMS 2022
EditorsLinlin Shen, Alejandro Rodriguez Gonzalez, KC Santosh, Zhihui Lai, Rosa Sicilia, Joao Rafael Almeida, Bridget Kane
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages137-142
Number of pages6
ISBN (Electronic)9781665467704
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event35th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2022 - Shenzhen, China
Duration: 21 Jul 202223 Jul 2022

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
Volume2022-July
ISSN (Print)1063-7125

Conference

Conference35th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2022
Country/TerritoryChina
CityShenzhen
Period21/07/2223/07/22

Keywords

  • 3D MRI segmentation
  • Car-diac
  • Medical Image
  • Ventricular segmentation

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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