Research on multi-Atlas segmentation based on feature clustering
Yan Kai1, Bai Xue2, Wang Binbing2, Shan Guoping2, Kang Xi1
1School of Nuclear Science and Technology, University of South China, Hengyang 421000, China; 2Department of Radiation Physics, Zhejiang Cancer Hospital, Cancer Hospital of University of Chinese Academy of Sciences,Institute of Cancer Research and Basic Medical Science of Chinese Academy of Science, Hangzhou 310022, China
Abstract：Objective To study the improvement of normal tissue region of interest (ROI) segmentation based on clustering-based multi-Atlas segmentation method, thereby achieving better delineation of organs at risk. Methods CT images of 100 patients with cervical cancer who had completed treatment in Zhejiang Cancer Hospital during 2019-2020 were selected as the Atlas database. According to the volume characteristic parameters of the organs at risk (bladder, rectum and outer contour), the Atlas database was divided into several subsets by k-means clustering algorithm. The image to be segmented was matched to the corresponding Atlas library for multi-Atlas segmentation. The dice similarity coefficient (DSC) was used to evaluate the segmentation results. Results Using 30 patients as the test set, the sub-Atlas generated by different clustering methods were compared for the improvement of image segmentation results. Compared with general multi-Atlas segmentation methods, clustering-based multi-Atlas segmentation method significantly improve the segmentation accuracy for the bladder (DSC=0.83±0.09 vs. 0.69±0.15, P<0.001) and the rectum (0.7±0.07 vs. 0.56±0.16, P<0.001), but no statistical significance was observed for left and right femoral head (0.92±0.04, 0.91±0.02) and bone marrow (0.91±0.06). The average segmentation time of clustering-based multi-Atlas segmentation method was shorter than that of the general multi-Atlas segmentation method (2.7 min vs. 6.3 min). Conclusion The clustering-based multi-Atlas segmentation method can not only reduce the number of Atlas images registered with the image to be segmented, but also can be expected to improve the segmentation effect and obtain higher accuracy.
Yan Kai,Bai Xue,Wang Binbing et al. Research on multi-Atlas segmentation based on feature clustering[J]. Chinese Journal of Radiation Oncology, 2023, 32(6): 533-538.
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