Cell Mitosis Event Analysis in Phase Contrast Microscopy Images using Deep Learning
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.
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
Keywords and Phrases
Cell mitosis event analysis; Convolutional neural networks; Long short term memory; Low-Rank matrix recovery
International Standard Serial Number (ISSN)
Article - Journal
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