Paper Info
Title
Hessian-assisted supervoxel: structure-oriented voxel clustering and application to mediastinal lymph node detection from CT volumes.
Abstract
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 methodu0027s 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.
Year
DOI
Venue
2017
10.1117/12.2254782
Proceedings of SPIE
Field
DocType
Volume
Voxel,Mediastinal lymph node,Computer vision,Segmentation,Hessian matrix,Feature extraction,Image segmentation,Artificial intelligence,Cluster analysis,False positive paradox,Physics
Conference
10134
ISSN
Citations 
PageRank 
0277-786X
0
0.34
References 
Authors
6
11
Name
Order
Citations
PageRank
Hirohisa Oda1458.30
Kanwal K. Bhatia2344.13
Masahiro Oda318240.81
Takayuki Kitasaka452067.91
Shingo Iwano5577.54
Hirotoshi Honma6309.77
Hirotsugu Takabatake723529.60
Masaki Mori8344.15
Hiroshi Natori922028.49
Julia A Schnabel101978151.49
Kensaku Mori111125160.28