Automated abdominal lymph node segmentation based on RST analysis and SVM
Yukitaka Nimura, Yuichiro Hayashi, Takayuki Kitasaka, Kazuhiro Furukawa, Kazunari Misawa, Kensaku Mori · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
This paper describes a segmentation method for abdominal lymph node (LN) using radial structure tensor analysis (RST) and support vector machine. LN analysis is one of crucial parts of lymphadenectomy, which is a surgical procedure to remove one or more LNs in order to evaluate them for the presence of cancer. Several works for automated LN detection and segmentation have been proposed. However, there are a lot of false positives (FPs). The proposed method consists of LN candidate segmentation and FP reduction. LN candidates are extracted using RST analysis in each voxel of CT scan. RST analysis can discriminate between difference local intensity structures without influence of surrounding structures. In FP reduction process, we eliminate FPs using support vector machine with shape and intensity information of the LN candidates. The experimental result reveals that the sensitivity of the proposed method was 82.0 % with 21.6 FPs/case.