A Hierarchical Convolutional Neural Network for Mitosis Detection in Phase-Contrast Microscopy Images
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.
Y. Mao and Z. Yin, "A Hierarchical Convolutional Neural Network for Mitosis Detection in Phase-Contrast Microscopy Images," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9901 LNCS, pp. 685-692, Springer Verlag, Jan 2016.
The definitive version is available at https://doi.org/10.1007/978-3-319-46723-8_79
19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI (2016: Oct. 17-21, Athens, Greece)
Intelligent Systems Center
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
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