Cell mitosis detection using deep neural networks

Quantitative analysis of cell mitosis, the process by which cells regenerate, is important in cell biology. Automatic cell mitosis detection can greatly facilitate the investigation of cell life cycle. However, cell-type diversity, cell non-rigid deformation and high cell density pose difficulties on handcrafting visual features for traditional approaches. Aided by massively captured microscopy image sequences, deep neural networks have recently become available for automatic cell mitosis detection. This paper proposes an end-to-end framework named as F3D-CNN for mitosis detection, and F3D-CNN is directly trained from data without requiring designing domain dependent features. Well-trained F3D-CNN first filters out potential mitosis events based on the static information in each individual image, and further discriminates candidates by incorporating the spatiotemporal information from image sequences. The state-of-the-art performance of F3D-CNN was confirmed in experiments on two public datasets (multipotent C3H10T1/2 mesenchymal stem cells and C2C12 myoblastic stem cells).

[1]  Yan Wang,et al.  Object Skeleton Extraction in Natural Images by Fusing Scale-Associated Deep Side Outputs , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[3]  Hayit Greenspan,et al.  Automatic detection of cell divisions (mitosis) in live-imaging microscopy images using Convolutional Neural Networks , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[4]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[5]  Zhang Yi,et al.  Symmetric low-rank representation for subspace clustering , 2014, Neurocomputing.

[6]  Hao Chen,et al.  Automated mitosis detection with deep regression networks , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[7]  Takeo Kanade,et al.  Computer vision tracking of stemness , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[8]  Philipp J. Keller,et al.  Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data , 2014, Nature Methods.

[9]  Zhang Yi,et al.  Protein secondary structure prediction by using deep learning method , 2017, Knowl. Based Syst..

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

[11]  Takeo Kanade,et al.  Automated Mitosis Detection of Stem Cell Populations in Phase-Contrast Microscopy Images , 2011, IEEE Transactions on Medical Imaging.

[12]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[13]  Zengbo Wang,et al.  Optical virtual imaging at 50 nm lateral resolution with a white-light nanoscope. , 2011, Nature communications.

[14]  Lei Zhang,et al.  Real-Time Compressive Tracking , 2012, ECCV.

[15]  Zhang Yi,et al.  Learning robust uniform features for cross-media social data by using cross autoencoders , 2016, Knowl. Based Syst..

[16]  한보형,et al.  Learning Deconvolution Network for Semantic Segmentation , 2015 .

[17]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Trevor Darrell,et al.  Hidden Conditional Random Fields , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[20]  Simon Lucey,et al.  Convolutional Sparse Coding for Trajectory Reconstruction , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Lorenzo Torresani,et al.  Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[22]  Hao Chen,et al.  Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks , 2016, AAAI.

[23]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[24]  Takeo Kanade,et al.  Mitosis sequence detection using hidden conditional random fields , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

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

[26]  Anne E. Carpenter,et al.  Symmetry-based mitosis detection in time-lapse microscopy , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[27]  Christian Wojek,et al.  Cell Event Detection in Phase-Contrast Microscopy Sequences from Few Annotations , 2015, MICCAI.

[28]  William J. Godinez,et al.  Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time. , 2009, Genome research.

[29]  Joakim Jalden,et al.  Global Linking of Cell Tracks Using the Viterbi Algorithm , 2015, IEEE Transactions on Medical Imaging.

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