Delving Deep into Liver Focal Lesion Detection: A Preliminary Study

Hepatocellular carcinoma (HCC) is the second most frequent cause of malignancy-related death and is one of the diseases with the highest incidence in the world. Because the liver is the only organ in the human body that is supplied by two major vessels: the hepatic artery and the portal vein, various types of malignant tumors can spread from other organs to the liver. And due to the liver masses' heterogeneous and diffusive shape, the tumor lesions are very difficult to be recognized, thus automatic lesion detection is necessary for the doctors with huge workloads. To assist doctors, this work uses the existing large-scale annotation medical image data to delve deep into liver lesion detection from multiple directions. To solve technical difficulties, such as the image-recognition task, traditional deep learning with convolution neural networks (CNNs) has been widely applied in recent years. However, this kind of neural network, such as Faster Regions with CNN features (R-CNN), cannot leverage the spatial information because it is applied in natural images (2D) rather than medical images (3D), such as computed tomography (CT) images. To address this issue, we propose a novel algorithm that is appropriate for liver CT imaging. Furthermore, according to radiologists' experience in clinical diagnosis and the characteristics of CT images of liver cancer, a liver cancer-detection framework with CNN, including image processing, feature extraction, region proposal, image registration, and classification recognition, was proposed to facilitate the effective detection of liver lesions.

[1]  Christopher Joseph Pal,et al.  Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..

[2]  Steven Henikoff,et al.  SIFT: predicting amino acid changes that affect protein function , 2003, Nucleic Acids Res..

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

[4]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[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]  J. Sharpe.  Optical projection tomography. , 2004, Annual review of biomedical engineering.

[7]  Zheng Zhang,et al.  MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems , 2015, ArXiv.

[8]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

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

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

[12]  G. Schlaug,et al.  The ischemic penumbra: operationally defined by diffusion and perfusion MRI. , 1999, Neurology.

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

[14]  Hao Chen,et al.  3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes , 2016, MICCAI.

[15]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[16]  Justin Salamon,et al.  Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification , 2016, IEEE Signal Processing Letters.

[17]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[18]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  J. Vane,et al.  Optical Projection Tomography as a Tool for 3D Microscopy and Gene Expression Studies , 2002 .

[20]  Anthony Gamst,et al.  Development of cortical and subcortical brain structures in childhood and adolescence: a structural MRI study , 2002 .

[21]  Ke Li,et al.  Prospective Evaluation of Reduced Dose Computed Tomography for the Detection of Low-Contrast Liver Lesions: Direct Comparison with Concurrent Standard Dose Imaging , 2017, European Radiology.

[22]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[23]  Xiaodong Cui,et al.  Data Augmentation for Deep Neural Network Acoustic Modeling , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[24]  T. Jernigan,et al.  Development of cortical and subcortical brain structures in childhood and adolescence: a structural MRI study , 2002, Developmental medicine and child neurology.

[25]  Konstantinos Kamnitsas,et al.  Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..

[26]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[28]  T. Ravikumar,et al.  Intraoperative ultrasonography of liver: detection of occult liver tumors and treatment by cryosurgery. , 1994, Cancer detection and prevention.

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

[30]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Soumith Chintala,et al.  A MultiPath Network for Object Detection , 2016, BMVC.

[32]  Abhinav Gupta,et al.  A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[34]  Ann Q. Gates,et al.  TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2005 .

[35]  C. Mathers,et al.  Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012 , 2015, International journal of cancer.

[36]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

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

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

[39]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

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