title: Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets.
abstract: OBJECTIVE
  Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images.
  
  
  METHODS
  The proposed method employs deep fully convolutional neural networks (CNNs) for organ detection and segmentation, which is further refined by a time-implicit multi-phase evolution method. Firstly, a 3D CNN is trained to automatically localize and delineate the organs of interest with a probability prediction map. The learned probability map provides both subject-specific spatial priors and initialization for subsequent fine segmentation. Then, for the refinement of the multi-organ segmentation, image intensity models, probability priors as well as a disjoint region constraint are incorporated into an unified energy functional. Finally, a novel time-implicit multi-phase level-set algorithm is utilized to efficiently optimize the proposed energy functional model.
  
  
  RESULTS
  Our method has been evaluated on 140 abdominal CT scans for the segmentation of four organs (liver, spleen and both kidneys). With respect to the ground truth, average Dice overlap ratios for the liver, spleen and both kidneys are 96.0, 94.2 and 95.4%, respectively, and average symmetric surface distance is less than 1.3 mm for all the segmented organs. The computation time for a CT volume is 125 s in average. The achieved accuracy compares well to state-of-the-art methods with much higher efficiency.
  
  
  CONCLUSIONS
  A fully automatic method for multi-organ segmentation from abdominal CT images was developed and evaluated. The results demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency.
authors.affiliation: School of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, China.
authors.forename: Peijun
authors.initials: P
authors.lastname: Hu
authors.affiliation: School of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, China.
authors.forename: Fa
authors.initials: F
authors.lastname: Wu
authors.affiliation: College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China.
authors.forename: Jialin
authors.initials: J
authors.lastname: Peng
authors.affiliation: School of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, China.
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Bao
authors.affiliation: Department of Radiology, First Affiliated Hospital, Zhejiang University, Hangzhou, 310003, China.
authors.forename: Feng
authors.initials: F
authors.lastname: Chen
authors.affiliation: School of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, China. dkong@zju.edu.cn.
authors.forename: Dexing
authors.identifier: http://orcid.org/0000-0003-3289-8954
authors.initials: D
authors.lastname: Kong
mesh_terms: D000005:Abdomen / Q000000981:diagnostic imaging; D000465:Algorithms; D001331:Automation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007668:Kidney / Q000000981:diagnostic imaging*; D008099:Liver / Q000000981:diagnostic imaging*; D016571:Neural Networks, Computer; D011336:Probability; D013154:Spleen / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: 3D CT; Deep CNN; Multi-organ segmentation; Time-implicit multi-phase level sets