SS-FS CSA: Self-Supervised and Fully Supervised Integration for 3D Cerebrovascular Segmentation

Chenxi Niu, Ziyu Liu, Xiangjian He

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

Abstract

Three-dimensional cerebrovascular segmentation is crucial for accurate diagnosis and treatment planning of cerebrovascular diseases.However, the lack of high-quality publicly labelled datasets can limit sufficient training, leading to inaccurate results.To address this issue, this study proposes a novel method that combines self-supervised and fully supervised learning, termed the SS-FS Cerebrovascular Segmentation Approach (SS-FS CSA).The method introduces publicly available unlabelled databases into the training process, alleviating the problem of insufficient high-quality labelled medical datasets.The SS-FS CSA method achieves a Dice Similarity Coefficient (DSC) of 82.82%, improving over 2% compared to the SOTA baseline, proving its validity and feasibility in 3D segmentation tasks.

Original languageEnglish
Title of host publicationProceedings of the 6th ACM International Conference on Multimedia in Asia, MMAsia 2024
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9798400712739
DOIs
Publication statusPublished - 28 Dec 2024
Event6th ACM International Conference on Multimedia in Asia, MMAsia 2024 - Auckland, New Zealand
Duration: 3 Dec 20246 Dec 2024

Publication series

NameProceedings of the 6th ACM International Conference on Multimedia in Asia, MMAsia 2024

Conference

Conference6th ACM International Conference on Multimedia in Asia, MMAsia 2024
Country/TerritoryNew Zealand
CityAuckland
Period3/12/246/12/24

Keywords

  • Cerebrovascular segmentation
  • deep learning
  • self-supervised learning
  • TOF-MRA

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction

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