3D‐SIFT‐Flow for atlas‐based CT liver image segmentation. Issue 5 (6th April 2016)

• Record Type: Journal Article
• Authors: Xu, Yan; Xu, Chenchao; Kuang, Xiao; Wang, Hongkai; Chang, Eric I‐Chao; Huang, Weimin; Fan, Yubo
• Is Part Of: Medical physics. Volume 43:Issue 5(2016)
• Page Start: 2229
• Page End: 2241
• Publication Date: 2016-04-06
• Subjects: biological tissues -- computerised tomography -- edge detection -- feature extraction -- image fusion -- image registration -- image segmentation -- liver -- medical image processing; Computed tomography -- Registration -- Segmentation; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Methods or arrangements for processing data by operating upon the order or content of the data handled -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; SIFT‐flow -- label transfer -- registration -- multiatlas -- segmentation; Liver -- Three dimensional image processing -- Medical image segmentation -- Computed tomography -- Flow visualization -- Optical flow -- Image registration -- Cancer -- Biomedical modeling; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4945021
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: In this paper, the authors proposed a new 3D registration algorithm, 3D‐scale invariant feature transform (SIFT)‐Flow, for multiatlas‐based liver segmentation in computed tomography (CT) images. Methods: In the registration work, the authors developed a new registration method that takes advantage of dense correspondence using the informative and robust SIFT feature. The authors computed the dense SIFT features for the source image and the target image and designed an objective function to obtain the correspondence between these two images. Labeling of the source image was then mapped to the target image according to the former correspondence, resulting in accurate segmentation. In the fusion work, the 2D‐based nonparametric label transfer method was extended to 3D for fusing the registered 3D atlases. Results: Compared with existing registration algorithms, 3D‐SIFT‐Flow has its particular advantage in matching anatomical structures (such as the liver) that observe large variation/deformation. The authors observed consistent improvement over widely adopted state‐of‐the‐art registration methods such asELASTIX, ANTS, and multiatlas fusion methods such as joint label fusion. Experimental results of liver segmentation on the MICCAI 2007 Grand Challenge are encouraging, e.g., Dice overlap ratio 96.27% ± 0.96% by our method compared with the previous state‐of‐the‐art result of 94.90% ± 2.86%. Conclusions: Experimental results show that 3D‐SIFT‐Flow is robust for segmenting the liver from CT images, which has large tissue deformation and blurry boundary, and 3D label transfer is effective and efficient for improving the registration accuracy.