Title

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.

Department(s)

Computer Science

Comments

This project was supported by National Science Foundation (NSF) CAREER award IIS-1351049 and NSF EPSCoR grant IIA-1355406.

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.

Share

 
COinS