Weakly Supervised Cell-Instance Segmentation with Two Types of Weak Labels by Single Instance Pasting

Kazuya Nishimura, Ryoma Bise

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

1 Citation (Scopus)

Abstract

Cell instance segmentation that recognizes each cell boundary is an important task in cell image analysis. While deep learning-based methods have shown promising performances with a certain amount of training data, most of them require full annotations that show the boundary of each cell. Generating the annotation for cell segmentation is time-consuming and human labor. To reduce the annotation cost, we propose a weakly supervised segmentation method using two types of weak labels (one for cell type and one for nuclei position). Unlike general images, these two labels are easily obtained in phase-contrast images. The intercellular boundary, which is necessary for cell instance segmentation, cannot be directly obtained from these two weak labels, so to generate the boundary information, we propose a single instance pasting based on the copy-and-paste technique. First, we locate single-cell regions by counting cells and store them in a pool. Then, we generate the intercel-lular boundary by pasting the stored single-cell regions to the original image. Finally, we train a boundary estimation network with the generated labels and perform instance segmentation with the network. Our evaluation on a public dataset demonstrated that the proposed method achieves the best performance among the several weakly supervised methods we compared.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3184-3193
Number of pages10
ISBN (Electronic)9781665493468
DOIs
Publication statusPublished - 2023
Event23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023 - Waikoloa, United States
Duration: Jan 3 2023Jan 7 2023

Publication series

NameProceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023

Conference

Conference23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023
Country/TerritoryUnited States
CityWaikoloa
Period1/3/231/7/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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