Spatial-Temporal Mitosis Detection in Phase-Contrast Microscopy via Likelihood Map Estimation by 3DCNN

Kazuya Nishimura, Ryoma Bise

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

抄録

Automated mitotic detection in time-lapse phase-contrast microscopy provides us much information for cell behavior analysis, and thus several mitosis detection methods have been proposed. However, these methods still have two problems; 1) they cannot detect multiple mitosis events when there are closely placed. 2) they do not consider the annotation gaps, which may occur since the appearances of mitosis cells are very similar before and after the annotated frame. In this paper, we propose a novel mitosis detection method that can detect multiple mitosis events in a candidate sequence and mitigate the human annotation gap via estimating spatial-temporal likelihood map by 3DCNN. In this training, the loss gradually decreases with the gap size between ground-truth and estimation. This mitigates the annotation gaps. Our method outperformed the compared methods in terms of F1-score using challenging dataset that contains the data under four different conditions. Code is publicly available in https://github.com/naivete5656/MDMLM.

本文言語英語
ホスト出版物のタイトル42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
ホスト出版物のサブタイトルEnabling Innovative Technologies for Global Healthcare, EMBC 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1811-1815
ページ数5
ISBN(電子版)9781728119908
DOI
出版ステータス出版済み - 7 2020
イベント42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, カナダ
継続期間: 7 20 20207 24 2020

出版物シリーズ

名前Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
2020-July
ISSN(印刷版)1557-170X

会議

会議42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Countryカナダ
CityMontreal
Period7/20/207/24/20

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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