title: Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection.
abstract: OBJECTIVE
  Cardiac computed tomography (CT) is widely used in clinical diagnosis of cardiovascular diseases. Whole heart segmentation (WHS) plays a vital role in developing new clinical applications of cardiac CT. However, the shape and appearance of the heart can vary greatly across different scans, making the automatic segmentation particularly challenging. The objective of this work is to develop and evaluate a multiatlas segmentation (MAS) scheme using a new atlas ranking and selection algorithm for automatic WHS of CT data. Research on different MAS strategies and their influence on WHS performance are limited. This work provides a detailed comparison study evaluating the impacts of label fusion, atlas ranking, and sizes of the atlas database on the segmentation performance.
  
  
  METHODS
  Atlases in a database were registered to the target image using a hierarchical registration scheme specifically designed for cardiac images. A subset of the atlases were selected for label fusion, according to the authors' proposed atlas ranking criterion which evaluated the performance of each atlas by computing the conditional entropy of the target image given the propagated atlas labeling. Joint label fusion was used to combine multiple label estimates to obtain the final segmentation. The authors used 30 clinical cardiac CT angiography (CTA) images to evaluate the proposed MAS scheme and to investigate different segmentation strategies.
  
  
  RESULTS
  The mean WHS Dice score of the proposed MAS method was 0.918 ± 0.021, and the mean runtime for one case was 13.2 min on a workstation. This MAS scheme using joint label fusion generated significantly better Dice scores than the other label fusion strategies, including majority voting (0.901 ± 0.276, p < 0.01), locally weighted voting (0.905 ± 0.0247, p < 0.01), and probabilistic patch-based fusion (0.909 ± 0.0249, p < 0.01). In the atlas ranking study, the proposed criterion based on conditional entropy yielded a performance curve with higher WHS Dice scores compared to the conventional schemes (p < 0.03). In the atlas database study, the authors showed that the MAS using larger atlas databases generated better performance curves than the MAS using smaller ones, indicating larger atlas databases could produce more accurate segmentation.
  
  
  CONCLUSIONS
  The authors have developed a new MAS framework for automatic WHS of CTA and investigated alternative implementations of MAS. With the proposed atlas ranking algorithm and joint label fusion, the MAS scheme is able to generate accurate segmentation within practically acceptable computation time. This method can be useful for the development of new clinical applications of cardiac CT.
authors.affiliation: SJTU-CU International Cooperative Research Center, Department of Engineering Mechanics, School of Naval Architecture Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Xiahai
authors.initials: X
authors.lastname: Zhuang
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queens Gate, London SW7 2AZ, United Kingdom.
authors.forename: Wenjia
authors.initials: W
authors.lastname: Bai
authors.affiliation: Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
authors.forename: Jingjing
authors.initials: J
authors.lastname: Song
authors.affiliation: Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
authors.forename: Songhua
authors.initials: S
authors.lastname: Zhan
authors.affiliation: SJTU-CU International Cooperative Research Center, Department of Engineering Mechanics, School of Naval Architecture Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
authors.forename: Xiaohua
authors.initials: X
authors.lastname: Qian
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queens Gate, London SW7 2AZ, United Kingdom.
authors.forename: Wenzhe
authors.initials: W
authors.lastname: Shi
authors.affiliation: Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.
authors.forename: Yanyun
authors.initials: Y
authors.lastname: Lian
authors.affiliation: Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queens Gate, London SW7 2AZ, United Kingdom.
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000792:Angiography / Q000379:methods*; D001271:Atlases as Topic; D002318:Cardiovascular Diseases / Q000000981:diagnostic imaging; D016208:Databases, Factual; D019277:Entropy; D005260:Female; D006321:Heart / Q000000981:diagnostic imaging*; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*