3D liver segmentation using multiple region appearances and graph cuts. Issue 12 (6th November 2015)

• Record Type: Journal Article
• Authors: Peng, Jialin; Hu, Peijun; Lu, Fang; Peng, Zhiyi; Kong, Dexing; Zhang, Hongbo
• Is Part Of: Medical physics. Volume 42:Issue 12(2015)
• Page Start: 6840
• Page End: 6852
• Publication Date: 2015-11-06
• Subjects: computerised tomography -- diseases -- graph theory -- image segmentation -- liver -- medical image processing -- tumours; Computed tomography -- Segmentation; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; liver segmentation -- graph cuts -- multiregion -- multiappearance -- CT; Liver -- Cancer -- Medical image segmentation -- Computed tomography -- Three dimensional image processing -- Computer modeling -- Geodesy -- Computer simulation; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4934834
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Efficient and accurate 3D liver segmentations from contrast‐enhanced computed tomography (CT) images play an important role in therapeutic strategies for hepatic diseases. However, inhomogeneous appearances, ambiguous boundaries, and large variance in shape often make it a challenging task. The existence of liver abnormalities poses further difficulty. Despite the significant intensity difference, liver tumors should be segmented as part of the liver. This study aims to address these challenges, especially when the target livers contain subregions with distinct appearances. Methods: The authors propose a novel multiregion‐appearance based approach with graph cuts to delineate the liver surface. For livers with multiple subregions, a geodesic distance based appearance selection scheme is introduced to utilize proper appearance constraint for each subregion. A special case of the proposed method, which uses only one appearance constraint to segment the liver, is also presented. The segmentation process is modeled with energy functions incorporating both boundary and region information. Rather than a simple fixed combination, an adaptive balancing weight is introduced and learned from training sets. The proposed method only calls initialization inside the liver surface. No additional constraints from user interaction are utilized. Results: The proposed method was validated on 50 3D CT images from three datasets, i.e., Medical Image Computing and Computer Assisted Intervention (MICCAI) training and testing set, and local dataset. On MICCAI testing set, the proposed method achieved a total score of 83.4 ± 3.1, outperforming nonexpert manual segmentation (average score of 75.0). When applying their method to MICCAI training set and local dataset, it yielded a mean Dice similarity coefficient (DSC) of 97.7% ± 0.5% and 97.5% ± 0.4%, respectively. These results demonstrated the accuracy of the method when applied to different computed tomography (CT) datasets. In addition, user operator variability experiments showed its good reproducibility. Conclusions: A multiregion‐appearance based method is proposed and evaluated to segment liver. This approach does not require prior model construction and so eliminates the burdens associated with model construction and matching. The proposed method provides comparable results with state‐of‐the‐art methods. Validation results suggest that it may be suitable for the clinical use.