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Computers in biology and medicine v.95, 2018년, pp.198 - 208   SCI SCIE SCOPUS
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Fully automatic liver segmentation in CT images using modified graph cuts and feature detection

Huang, Qing (Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China ) ; Ding, Hui (Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China ) ; Wang, Xiaodong (Department of Interventional Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China ) ; Wang, Guangzhi (Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China ) ;
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    Abstract Purpose Liver segmentation from CT images is a fundamental step in trajectory planning for computer-assisted interventional surgery. In this paper, we present a fully automatic procedure using modified graph cuts and feature detection for accurate and fast liver segmentation. Methods The initial slice and seeds of graph cuts are automatically determined using an intensity-based method with prior position information. A contrast term based on the similarities and differences of local organs across multi-slices is proposed to enhance the weak boundaries of soft organs and to prevent over-segmentation. The term is then integrated into the graph cuts for automatic slice segmentation. Patient-specific intensity and shape constraints of neighboring slices are also used to prevent leakage. Finally, a feature detection method based on vessel anatomical information is proposed to eliminate the adjacent inferior vena cava with similar intensities. Results We performed experiments on 20 Sliver07, 20 3Dircadb datasets and local clinical datasets. The average volumetric overlap error, volume difference, symmetric surface distance and volume processing time were 5.3%, −0.6%, 1.0 mm, 17.8 s for the Sliver07 dataset and 8.6%, 0.7%, 1.6 mm, 12.7 s for the 3Dircadb dataset, respectively. Conclusions The proposed method can effectively extract the liver from low contrast and complex backgrounds without training samples. It is fully automatic, accurate and fast for liver segmentation in clinical settings. Highlights A fully automatic and fast liver segmentation procedure for CT images is proposed. A contrast term is integrated in the graph cuts to enhance its segmentation ability in weak boundary and prevent leakage. A feature detection method is proposed to identify and remove redundant vessels after graph cuts. The procedure can effectively prevent leakage and proved to be accurate and fast for liver segmentation in clinical. Graphical abstract [DISPLAY OMISSION]


  • 주제어

    Liver segmentation .   Graph cuts .   Adaptive shape constraint.  

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