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Oda, H.* ; Bhatia, K.K.* ; Oda, M.* ; Kitasaka, T.* ; Iwano, S.* ; Homma, H.* ; Takabatake, H.* ; Mori, M.* ; Natori, H.* ; Schnabel, J.A.* ; Mori, K.*

Hessian-assisted supervoxel: Structure-oriented voxel clustering and application to mediastinal lymph node detection from CT volumes.

In:. Wellingham: SPIE, 2017. (Progr. Biomed. Opt. Imaging ; 10134)
DOI
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.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Clustering ; Computer Aided Detection ; Feature Extraction
ISSN (print) / ISBN 1605-7422
Zeitschrift Progress in Biomedical Optics and Imaging
Quellenangaben Band: 10134 Heft: , Seiten: , Artikelnummer: , Supplement: ,
Verlag SPIE
Verlagsort Wellingham
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)