SSP-REGULARIZER: A STAR SHAPE PRIOR BASED REGULARIZER FOR VESSEL LUMEN SEGMENTATION IN OCT IMAGES

Chen Zhao, HUAIZHONG ZHANG, Junyuan Wang, Fuqiang Liu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

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Abstract

Optical coherence tomography (OCT) is widely used in high resolution imaging of biological tissues, which can help diagnose coronary heart disease by segmenting the vessel
lumen at the pixel-level. However, the lumen shape geometry is not well used in the state-of-the-art techniques for OCT image segmentation, especially the data-driven methods, leaving much room for performance improvement if some geometric features could be exploited to provide prior information. Thanks to the star shape geometry of vessel lumen, in this paper, a new Star Shape Prior based Regularizer (SSP-Regularizer) is proposed to improve segmentation performance. To validate its effectiveness, the proposed SSPRegularizer is applied to improve the optimization scheme used in Mask-RCNN for vessel lumen segmentation. Experimental results show that superior performance is achieved
with SSP-Regularizer, indicating its potentials in OCT imagery and optimization schemes.
Original languageEnglish
Title of host publicationIEEE International Conference on Image Processing 2022
Place of PublicationIEEE
PublisherIEEE Computer Society
Pages3106-3110
ISBN (Electronic)9781665496216
ISBN (Print)9781665496209
DOIs
Publication statusPublished - 18 Oct 2022

Keywords

  • Star shape prior
  • vessel lumen segmentation
  • optical coherence tomography
  • SSP-Regularizer
  • Mask-RCNN

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