Efficient semantic segmentation for computational pathology

Doanh C. Bui, Chang Su Kim, Jin Tae Kwak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In digital and computational pathology, semantic segmentation can be considered as the first step toward assessing tissue specimens, providing the essential information for various downstream tasks. There exist numerous semantic segmentation methods and these often face challenges as they are applied to whole slide images, which are high-resolution and gigapixel-sized, and thus require a large amount of computation. In this study, we investigate the feasibility of an efficient semantic segmentation approach for whole slide images, which only processes the low-resolution pathology images to obtain the semantic segmentation results as equivalent as the results that can be attained by using high-resolution images. We employ five advanced semantic segmentation models and conduct three types of experiments to quantitatively and qualitatively test the feasibility of the efficient semantic segmentation approach. The quantitative experimental results demonstrate that, provided with low-resolution images, the semantic segmentation methods are inferior to those with high-resolution images. However, using low-resolution images, there is a substantial reduction in the computational cost. Furthermore, the qualitative analysis shows that the results obtained from low-resolution images are comparable to those from high-resolution images, suggesting the feasibility of the low-to-high semantic segmentation in computational pathology.

Original languageEnglish
Title of host publicationMedical Imaging 2024
Subtitle of host publicationDigital and Computational Pathology
EditorsJohn E. Tomaszewski, Aaron D. Ward
PublisherSPIE
ISBN (Electronic)9781510671706
DOIs
Publication statusPublished - 2024
EventMedical Imaging 2024: Digital and Computational Pathology - San Diego, United States
Duration: 2024 Feb 192024 Feb 21

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12933
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2024: Digital and Computational Pathology
Country/TerritoryUnited States
CitySan Diego
Period24/2/1924/2/21

Bibliographical note

Publisher Copyright:
© 2024 SPIE.

Keywords

  • efficient computation
  • low resolution
  • semantic segmentation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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