Cell tracking with deep learning for cell detection and motion estimation in low-frame-rate

Junya Hayashida, Ryoma Bise

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

1 Citation (Scopus)

Abstract

Cell behavior analysis in high-throughput biological experiments is important for research and discovery in biology and medicine. To perform the high-throughput experiments, it requires to capture images in low frame rate in order to record images on multi-points. In such a low frame rate image sequence, movements of cells between successive frames are often larger than distances to nearby cells, and thus current methods based on proximity do not work properly. In this study, we propose a cell tracking method that enables to track cells in low frame rate by simultaneously estimating all of the cell motions in successive frames. In the experiments under dense conditions in low frame rate, our method outperformed the other methods.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer
Pages397-405
Number of pages9
ISBN (Print)9783030322380
DOIs
Publication statusPublished - Jan 1 2019
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 13 2019Oct 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11764 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/13/1910/17/19

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Hayashida, J., & Bise, R. (2019). Cell tracking with deep learning for cell detection and motion estimation in low-frame-rate. In D. Shen, P-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 397-405). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11764 LNCS). Springer. https://doi.org/10.1007/978-3-030-32239-7_44