基于CNN的相衬显微图像序列的癌细胞多目标跟踪

doi:10.11896/j.issn.1002-137X.2019.05.043

摘要/Abstract

摘要： 检测与跟踪相衬显微图像序列下的癌细胞对于分析癌细胞的生命周期以及开发抗癌新药具有非常重要的意义。传统的目标跟踪方法大多应用于刚性目标跟踪或单目标跟踪,而癌细胞是非刚性且不断裂变的多目标,这就大大增加了跟踪的难度。文中以相衬显微图像序列中的膀胱癌细胞为研究对象,提出一种基于卷积神经网络(Convolutional Neural Network,CNN)的癌细胞多目标跟踪方法。该算法采用基于检测的多目标跟踪方法,首先利用深度学习检测框架Faster R-CNN卷积神经网络实现癌细胞的检测,初步获得待跟踪的癌细胞;再利用扫描圆算法(Circle Scanning Algorithm,CSA)实现黏连细胞的检测优化,进一步提高黏连区域的细胞检测精度;最后提取综合特征描述子,对卷积特征、尺寸特征和位置特征进行加权求和,实现跟踪目标的综合描述,从而实现不同帧癌细胞间的高效关联匹配,最终实现癌细胞的多目标跟踪。一系列实验结果表明,相较于传统方法,所提方法不仅在癌细胞的检测和跟踪上性能有较大的提升,而且可以有效处理目标的遮挡问题。

关键词: FasterR-CNN, 多目标跟踪, 卷积神经网络, 扫描圆算法, 细胞检测

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

中图分类号:

TP183

胡海根, 周莉莉, 周乾伟, 陈胜勇, 张俊康. 基于CNN的相衬显微图像序列的癌细胞多目标跟踪[J]. 计算机科学, 2019, 46(5): 279-285. https://doi.org/10.11896/j.issn.1002-137X.2019.05.043

HU Hai-gen, ZHOU Li-li, ZHOU Qian-wei, CHEN Sheng-yong, ZHANG Jun-kang. Multi-target Tracking of Cancer Cells under Phase Contrast Microscopic Images Based
on Convolutional Neural Network[J]. Computer Science, 2019, 46(5): 279-285. https://doi.org/10.11896/j.issn.1002-137X.2019.05.043

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