Consistent Cell Tracking in Multi-frames with Spatio-Temporal Context by Object-Level Warping Loss

Junya Hayashida, Kazuya Nishimura, Ryoma Bise

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

2 Citations (Scopus)

Abstract

Multi-object tracking is essential in biomedical image analysis. Most methods follow a tracking-by-detection approach that involves using object detectors and learning the appearance feature models of the detected regions for association. Although these methods can learn the appearance similarity features to identify the same objects among frames, they have difficulties identifying the same cells because cells have a similar appearance and their shapes change as they migrate. In addition, cells often partially overlap for several frames. In this case, even an expert biologist would require knowledge of the spatial-temporal context in order to identify individual cells. To tackle such difficult situations, we propose a cell-tracking method that can effectively use the spatial-temporal context in multiple frames by using long-term motion estimation and an object-level warping loss. We conducted experiments showing that the proposed method outperformed state-of-the-art methods under various conditions on real biological images.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1759-1768
Number of pages10
ISBN (Electronic)9781665409155
DOIs
Publication statusPublished - 2022
Event22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022 - Waikoloa, United States
Duration: Jan 4 2022Jan 8 2022

Publication series

NameProceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022

Conference

Conference22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022
Country/TerritoryUnited States
CityWaikoloa
Period1/4/221/8/22

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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