Qing Huang1, Hui Ding2, Xiaodong Wang3, Guangzhi Wang4. 1. Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China. Electronic address: q-huang12@mails.tsinghua.edu.cn. 2. Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China. Electronic address: dinghui@tsinghua.edu.cn. 3. Department of Interventional Radiology, Peking University Cancer Hospital & Institute, Beijing 100142, China. Electronic address: tigat@126.com. 4. Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China. Electronic address: wgz-dea@tsinghua.edu.cn.
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.
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.
Authors: D Seppelt; T Ittermann; M L Kromrey; C Kolb; C vWahsen; P Heiss; H Völzke; R T Hoffmann; J P Kühn Journal: Sci Rep Date: 2022-01-24 Impact factor: 4.379