Fully convolutional neural network with post-processing methods for automatic liver segmentation from CT

Automatic liver segmentation from abdominal Computed Tomography (CT) is an important step for hepatic disease diagnosis. It is a challenging task owing to the similarity between liver and its adjacent organs and the low contrast of liver texture (e.g. tumors and blood veins). In this paper, we propose a cascaded structure to automatically segment liver in CT scans. First, we train a fully convolutional neural network (FCN) for coarse liver segmentation; second, we make a comparative study for the performance of different classical segmentation models as the post-processing step to refine the liver segmentation, such as graph cut based method, level set based method and conditional random field (CRF). The main contributions are: (1) the enhancement of FCN for better liver segmentation; (2) the first comparative study on the performance of different classical segmentation models as the post-processing step. Our proposed model is validated on the commonly used database 3DIRCADb, and the experimental results demonstrate that our model excels other models.

[1]  Rachid Deriche,et al.  A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape , 2007, International Journal of Computer Vision.

[2]  Ye Wang,et al.  Liver segmentation with constrained convex variational model , 2014, Pattern Recognit. Lett..

[3]  Jialin Peng,et al.  A region-appearance-based adaptive variational model for 3D liver segmentation. , 2014, Medical physics.

[4]  Xinjian Chen,et al.  Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images , 2015, IEEE Transactions on Image Processing.

[5]  Lixu Gu,et al.  A new segmentation framework based on sparse shape composition in liver surgery planning system. , 2013, Medical physics.

[6]  Yadong Wang,et al.  Shape–intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images , 2016, International Journal of Computer Assisted Radiology and Surgery.

[7]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

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

[9]  Toshiya Nakaguchi,et al.  Liver Segmentation Approach Using Graph Cuts and Iteratively Estimated Shape and Intensity Constrains , 2012, MICCAI.

[10]  Lisa Tang,et al.  Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation , 2015, MICCAI.

[11]  S. Casciaro,et al.  Fully Automatic Liver Segmentation through Graph-Cut Technique , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Seyed-Ahmad Ahmadi,et al.  Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields , 2016, MICCAI.

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

[14]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[15]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[16]  Akinobu Shimizu,et al.  A conditional statistical shape model with integrated error estimation of the conditions; Application to liver segmentation in non-contrast CT images , 2014, Medical Image Anal..

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

[18]  Fang Lu,et al.  Automatic 3D liver location and segmentation via convolutional neural network and graph cut , 2016, International Journal of Computer Assisted Radiology and Surgery.

[19]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[20]  Martin Styner,et al.  Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets , 2009, IEEE Transactions on Medical Imaging.

[21]  Jacques Ferlay,et al.  GLOBOCAN 2012: Estimated cancer incidence, mortality and prevalence worldwide in 2012 , 2013 .

[22]  Olivier Ecabert,et al.  Automatic Model-Based Segmentation of the Heart in CT Images , 2008, IEEE Transactions on Medical Imaging.

[23]  Bodo Rosenhahn,et al.  Automatic 3D Liver Segmentation Using Sparse Representation of Global and Local Image Information via Level Set Formulation , 2015, ArXiv.