Computer Science ›› 2019, Vol. 46 ›› Issue (5): 279-285.doi: 10.11896/j.issn.1002-137X.2019.05.043

Special Issue: Medical Imaging

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Multi-target Tracking of Cancer Cells under Phase Contrast Microscopic Images Based
on Convolutional Neural Network

HU Hai-gen1, ZHOU Li-li1, ZHOU Qian-wei1, CHEN Sheng-yong1,2, ZHANG Jun-kang1   

  1. (College of Computer Science & Technology,Zhejiang University of Technology,Hangzhou 310024,China)1
    (School of Computer Science and Engineering,Tianjin University of Technology,Tianjin 300384,China)2
  • Published:2019-05-15

Abstract: Detecting and tracking cancer cells under phase contrast microscopic images plays a critical role for analyzing the life cycle of cancer cells and developing new anti-cancer drugs.Traditional target tracking methods are mostly applied to rigid target tracking or single target tracking,while cancer cells are non-rigid multiple targets with constant fission,and it makes tracking more challenging.Taking bladder cancer cells in the sequence of phase contrast micrographs images as research object,this paper proposed a multi-target tracking method of cancer cells based on convolutional neural network.Firstly,through making use of detection-based multi-target method,the proposed algorithm adopted the deep learning detection framework Faster R-CNN to detect the bladder cancer cells and preliminarily obtain the cancer cells to be tracked.Then CSA (circle scanning algorithm) was utilized to further optimize the detection of adhesion cancer cells,and further improve the detection accuracy of cells in adhesion area.Finally,it integrated the features of convolution,size and position into a synthetic feature descriptor by using weighting methods,thus tracking multiple cancer cells by achieving the efficient correlation and matching of different frames of cancer cells.The results of a series of experiments show that this method can not only improve the accuracy of detecting and tracking cancer cell,but also deal with the occlusion problem effectively.

Key words: Cell detection, Circle scanning algorithm, Convolutional neural network, Faster R-CNN, Multi-target tracking

CLC Number: 

  • TP183
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