Automatic 3D liver location and segmentation via convolutional neural network and graph cut.
OBJECTIVE
Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans.


METHODS
The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map.


RESULTS
The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and 33.14 mm, respectively.


CONCLUSIONS
The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method.
School of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, China.
Fang
F
Lu
School of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, China.
Fa
F
Wu
School of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, China.
Peijun
P
Hu
Department of Radiology, First Affiliated Hospital of Zhejiang University, Hangzhou, 310003, China.
Zhiyi
Z
Peng
School of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, China. dkong@zju.edu.cn.
Dexing
http://orcid.org/0000-0003-3289-8954
D
Kong
D000005:Abdomen; D000465:Algorithms; D016208:Databases, Factual; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging*; D016571:Neural Networks, Computer; D009929:Organ Size; D014057:Tomography, X-Ray Computed / Q000379:methods
3D convolution neural network; CT images; Graph cut; Liver segmentation