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.