Title

A Hierarchical Convolutional Neural Network for Mitosis Detection in Phase-Contrast Microscopy Images

Abstract

We propose a Hierarchical Convolution Neural Network (HCNN) for mitosis event detection in time-lapse phase contrast microscopy. Our method contains two stages: first,we extract candidate spatial-temporal patch sequences in the input image sequences which potentially contain mitosis events. Then,we identify if each patch sequence contains mitosis event or not using a hieratical convolutional neural network. In the experiments,we validate the design of our proposed architecture and evaluate the mitosis event detection performance. Our method achieves 99.1% precision and 97.2% recall in very challenging image sequences of multipolar-shaped C3H10T1/2 mesenchymal stem cells and outperforms other state-of-the-art methods. Furthermore,the proposed method does not depend on hand-crafted feature design or cell tracking. It can be straightforwardly adapted to event detection of other different cell types.

Department(s)

Computer Science

Keywords and Phrases

Cell Culture; Computer Vision; Convolution; Neural Networks; Stem Cells; Convolution Neural Network; Convolutional Neural Network; Mesenchymal Stem Cell; Mitosis Detections; Phase-Contrast Microscopy; Proposed Architectures; Spatial Temporals; State-of-the-art Methods; Medical Imaging

International Standard Book Number (ISBN)

9783319467221

International Standard Serial Number (ISSN)

0302-9743

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Springer Verlag, All rights reserved.

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