Gland Instance Segmentation by Deep Multichannel Neural Networks

In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images. This is a task called instance segmentation that has recently become increasingly important. The problem is challenging since not only do the glands need to be segmented from the complex background, they are also required to be individually identified. Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional, location and boundary patterns in gland histology images. Our proposed system, deep multichannel framework, alleviates heavy feature design due to the use of convolutional neural networks and is able to meet multifarious requirement by altering channels. Compared to methods reported in the 2015 MICCAI Gland Segmentation Challenge and other currently prevalent methods of instance segmentation, we observe state-of-the-art results based on a number of evaluation metrics.

[1]  Fabian Rudolf,et al.  Accurate cell segmentation in microscopy images using membrane patterns , 2014, Bioinform..

[2]  Yi Li,et al.  Instance-Sensitive Fully Convolutional Networks , 2016, ECCV.

[3]  Stefan Roth,et al.  Efficient Multi-cue Scene Segmentation , 2013, GCPR.

[4]  Jian Sun,et al.  Instance-Aware Semantic Segmentation via Multi-task Network Cascades , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Yan Wang,et al.  DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Ronan Collobert,et al.  Learning to Segment Object Candidates , 2015, NIPS.

[8]  Martin Urschler,et al.  Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation , 2015, ArXiv.

[9]  Yang Li,et al.  Gland Instance Segmentation by Deep Multichannel Side Supervision , 2016, MICCAI.

[10]  Luca Maria Gambardella,et al.  Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.

[11]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[12]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[13]  Dumitru Erhan,et al.  Scalable Object Detection Using Deep Neural Networks , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Victor S. Lempitsky,et al.  N4-Fields: Neural Network Nearest Neighbor Fields for Image Transforms , 2014, ArXiv.

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

[17]  Vladlen Koltun,et al.  Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.

[18]  Ronan Collobert,et al.  Learning to Refine Object Segments , 2016, ECCV.

[19]  Jitendra Malik,et al.  Hypercolumns for object segmentation and fine-grained localization , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Vibhav Vineet,et al.  Conditional Random Fields as Recurrent Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

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

[22]  Sven J. Dickinson,et al.  Multi-cue Mid-level Grouping , 2014, ACCV.

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

[24]  Nasir M. Rajpoot,et al.  A Stochastic Polygons Model for Glandular Structures in Colon Histology Images , 2015, IEEE Transactions on Medical Imaging.

[25]  Camille Couprie,et al.  Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Zhuowen Tu,et al.  Deeply-Supervised Nets , 2014, AISTATS.

[27]  Guosheng Lin,et al.  CRF Learning with CNN Features for Image Segmentation , 2015, Pattern Recognit..

[28]  Jitendra Malik,et al.  Simultaneous Detection and Segmentation , 2014, ECCV.

[29]  Hao Chen,et al.  DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Hao Chen,et al.  Gland segmentation in colon histology images: The glas challenge contest , 2016, Medical Image Anal..

[31]  Jianbo Shi,et al.  DeepEdge: A multi-scale bifurcated deep network for top-down contour detection , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[34]  Thomas S. Huang,et al.  Learning Multi-channel Deep Feature Representations for Face Recognition , 2015, FE@NIPS.