3 March 2017 Hessian-assisted supervoxel: structure-oriented voxel clustering and application to mediastinal lymph node detection from CT volumes
Author Affiliations +
In this paper, we propose a novel supervoxel segmentation method designed for mediastinal lymph node by embedding Hessian-based feature extraction. Starting from a popular supervoxel segmentation method, SLIC, which computes supervoxels by minimising differences of intensity and distance, we overcome this method's limitation of merging neighboring regions with similar intensity by introducing Hessian-based feature analysis into the supervoxel formation. We call this structure-oriented voxel clustering, which allows more accurate division into distinct regions having blob-, line- or sheet-like structures. This way, different tissue types in chest CT volumes can be segmented individually, even if neighboring tissues have similar intensity or are of non- spherical extent. We demonstrate the performance of the Hessian-assisted supervoxel technique by applying it to mediastinal lymph node detection in 47 chest CT volumes, resulting in false positive reductions from lymph node candidate regions. 89 % of lymph nodes whose short axis is at least 10 mm could be detected with 5.9 false positives per case using our method, compared to our previous method having 83 % of detection rate with 6.4 false positives per case.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hirohisa Oda, Hirohisa Oda, Kanwal K. Bhatia, Kanwal K. Bhatia, Masahiro Oda, Masahiro Oda, Takayuki Kitasaka, Takayuki Kitasaka, Shingo Iwano, Shingo Iwano, Hirotoshi Homma, Hirotoshi Homma, Hirotsugu Takabatake, Hirotsugu Takabatake, Masaki Mori, Masaki Mori, Hiroshi Natori, Hiroshi Natori, Julia A. Schnabel, Julia A. Schnabel, Kensaku Mori, Kensaku Mori, } "Hessian-assisted supervoxel: structure-oriented voxel clustering and application to mediastinal lymph node detection from CT volumes", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101341D (3 March 2017); doi: 10.1117/12.2254782; https://doi.org/10.1117/12.2254782

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