Liver Tumor Segmentation of CT Image by Using Deep Fully Convolutional Network

[1]  Edward Wiebe,et al.  The Value of “Liver Windows” Settings in the Detection of Small Renal Cell Carcinomas on Unenhanced Computed Tomography , 2014, Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes.

[2]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[3]  N. Otsu A threshold selection method from gray level histograms , 1979 .

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

[5]  S. Casciaro,et al.  A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans , 2008, European Radiology.

[6]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[7]  Sebastian J. Schlecht,et al.  Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks , 2017, ArXiv.

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

[9]  Luc Van Gool,et al.  Detection-aided liver lesion segmentation using deep learning , 2017, NIPS 2017.

[10]  Zhuowen Tu,et al.  Holistically-Nested Edge Detection , 2015, ICCV.

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

[12]  L. R. Dice Measures of the Amount of Ecologic Association Between Species , 1945 .

[13]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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