Cell Mitosis Event Analysis in Phase Contrast Microscopy Images using Deep Learning
Abstract
In this paper, we solve the problem of mitosis event localization and its stage localization in time-lapse phase-contrast microscopy images. Our method contains three steps: first, we formulate a Low-Rank Matrix Recovery (LRMR) model to find salient regions from microscopy images and extract candidate patch sequences, which potentially contain mitosis events; second, we classify each candidate patch sequence by our proposed Hierarchical Convolution Neural Network (HCNN) with visual appearance and motion cues; third, for the detected mitosis sequences, we further segment them into four temporal stages by our proposed Two-stream Bidirectional Long-Short Term Memory (TS-BLSTM). In the experiments, we validate our system (LRMR, HCNN, and TS-BLSTM) and evaluate the mitosis event localization and stage localization performance. The proposed method outperforms state-of-the-arts by achieving 99.2% precision and 98.0% recall for mitosis event localization and 0.62 frame error on average for mitosis stage localization in five challenging image sequences.
Recommended Citation
Y. Mao et al., "Cell Mitosis Event Analysis in Phase Contrast Microscopy Images using Deep Learning," Medical Image Analysis, vol. 57, pp. 32 - 43, Elsevier B.V., Oct 2019.
The definitive version is available at https://doi.org/10.1016/j.media.2019.06.011
Department(s)
Computer Science
Keywords and Phrases
Cell mitosis event analysis; Convolutional neural networks; Long short term memory; Low-Rank matrix recovery
International Standard Serial Number (ISSN)
1361-8415
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2019 Elsevier B.V., All rights reserved.
Publication Date
01 Oct 2019
Comments
This project was supported by National Science Foundation (NSF) CAREER award IIS-1351049 and NSF EPSCoR grant IIA-1355406.