Detection and Recognition for Life State of Cell Cancer Using Two-Stage Cascade CNNs

Cancer cell detection and its stages recognition of life cycle are an important step to analyze cellular dynamics in the automation of cell based-experiments. In this work, a two-stage hierarchical method is proposed to detect and recognize different life stages of bladder cells by using two cascade Convolutional Neural Networks (CNNs). Initially, a hybrid object proposal algorithm (called EdgeSelective) by combining EdgeBoxes and Selective Search is proposed to generate candidate object proposals instead of a single Selective Search method in Region-CNN (R-CNN), and it can exploit the advantages of different mechanisms for generating proposals so that each cell in the image can be fully contained by at least one proposed region during the detection process. Then, the obtained cells from the previous step are used to train and extract features by employing CNNs for the purpose of cell life stage recognition. Finally, a series of comparison experiments are implemented. The results show that the proposed method can obtain better performance than traditional methods either in the stage of cell detection or cell life stage recognition, and it encourages and suggests the application in the development of new anticancer drug and cytopathology analysis of cancer patients in the near future.

[1]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Luca Maria Gambardella,et al.  Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks , 2013, MICCAI.

[3]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[4]  Martin Burger,et al.  Mathematical imaging methods for mitosis analysis in live-cell phase contrast microscopy , 2016, Methods.

[5]  Shengyong Chen,et al.  Rich feature hierarchies for cell detecting under phase contrast microscopy images , 2015, 2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP).

[6]  Bernt Schiele,et al.  Taking a deeper look at pedestrians , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Jeffrey F. Naughton,et al.  Generalized Search Trees for Database Systems , 1995, VLDB.

[9]  Andrew Zisserman,et al.  Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.

[10]  Ting Chen,et al.  Deep Learning Based Automatic Immune Cell Detection for Immunohistochemistry Images , 2014, MLMI.

[11]  R. Kiss,et al.  Videomicroscopic extraction of specific information on cell proliferation and migration in vitro. , 2008, Experimental cell research.

[12]  Jonathan T. Barron,et al.  Multiscale Combinatorial Grouping , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Jianwei Zhang,et al.  Hierarchical Mergence Approach to Cell Detection in Phase Contrast Microscopy Images , 2014, Comput. Math. Methods Medicine.

[14]  Jitendra Malik,et al.  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[16]  Bernt Schiele,et al.  What Makes for Effective Detection Proposals? , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  C. Lawrence Zitnick,et al.  Edge Boxes: Locating Object Proposals from Edges , 2014, ECCV.

[20]  Cristian Sminchisescu,et al.  CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Jean Ponce,et al.  Unsupervised Object Discovery and Tracking in Video Collections , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[22]  Thomas Deselaers,et al.  Measuring the Objectness of Image Windows , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  James M. Rehg,et al.  RIGOR: Reusing Inference in Graph Cuts for Generating Object Regions , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Gang Hua,et al.  A convolutional neural network cascade for face detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[26]  Cordelia Schmid,et al.  Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Luc Van Gool,et al.  SEEDS: Superpixels Extracted Via Energy-Driven Sampling , 2012, International Journal of Computer Vision.

[28]  Andrew Zisserman,et al.  Learning to Detect Cells Using Non-overlapping Extremal Regions , 2012, MICCAI.

[29]  Luca Maria Gambardella,et al.  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Flexible, High Performance Convolutional Neural Networks for Image Classification , 2022 .

[30]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[31]  Luc Van Gool,et al.  The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.

[32]  Sanja Fidler,et al.  segDeepM: Exploiting segmentation and context in deep neural networks for object detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Cordelia Schmid,et al.  Weakly Supervised Learning of Interactions between Humans and Objects , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Vladlen Koltun,et al.  Geodesic Object Proposals , 2014, ECCV.

[35]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[36]  Mu Zhu,et al.  A Relationship between the Average Precision and the Area Under the ROC Curve , 2015, ICTIR.

[37]  Philip H. S. Torr,et al.  BING: Binarized normed gradients for objectness estimation at 300fps , 2014, Computational Visual Media.

[38]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[39]  Jian Sun,et al.  Convolutional feature masking for joint object and stuff segmentation , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).