3D Structure Modeling of Dense Capillaries by Multi-objects Tracking

Ryoma Bise, Imari Sato, Kentaro Kajiya, Toyonobu Yamashita

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

2 被引用数 (Scopus)

抄録

A newly developed imaging technique called light-sheet laser microscopy imaging can visualize the detailed 3D structures of capillaries. Capillaries form complicated network structures in the obtained data, and this makes it difficult to model vessel structures by existing methods that implicitly assume simple tree structures for blood vessels. To cope with such dense capillaries with network structures, we propose to track the flow of blood vessels along a base-axis using a multiple-object tracking framework. We first track multiple blood vessels in cross-sectional images along a single axis to make the trajectories of blood vessels, and then connect these blood vessels to reveal their entire structures. This framework is efficient to track densely distributed vessels since it uses only a single cross-sectional plane. The network structure is then generated in the post-processing by connecting blood vessels on the basis of orientations of the trajectories. The results of experiments using a challenging real data-set demonstrate the efficacy of the proposed method, which are capable of modeling dense capillaries.

本文言語英語
ホスト出版物のタイトルProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
出版社IEEE Computer Society
ページ1333-1341
ページ数9
ISBN(電子版)9781467388504
DOI
出版ステータス出版済み - 12 16 2016
外部発表はい
イベント29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016 - Las Vegas, 米国
継続期間: 6 26 20167 1 2016

出版物シリーズ

名前IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN(印刷版)2160-7508
ISSN(電子版)2160-7516

その他

その他29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
国/地域米国
CityLas Vegas
Period6/26/167/1/16

All Science Journal Classification (ASJC) codes

  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学

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