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

Junya Hayashida, Ryoma Bise

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

8 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
編集者Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
出版社Springer
ページ397-405
ページ数9
ISBN(印刷版)9783030322380
DOI
出版ステータス出版済み - 2019
イベント22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, 中国
継続期間: 10 13 201910 17 2019

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11764 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

会議

会議22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
国/地域中国
CityShenzhen
Period10/13/1910/17/19

All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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