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TBS: Tensor-based supervoxels for unfolding the heart

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

Hirohisa Oda, Holger R. Roth, Kanwal Bhatia, Masahiro Oda, Takayuki Kitasaka, Toshiaki Akita, Julia Schnabel, Kensaku Mori

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
PublisherSpringer Verlag
Pages681-689
Number of pages9
Volume10433 LNCS
ISBN (Print)9783319661810
DOIs
Publication statusE-pub ahead of print - 4 Sep 2017
Event20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 11 Sep 201713 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10433 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period11/09/201713/09/2017

King's Authors

Abstract

Investigation of the myofiber structure of the heart is desired for studies of anatomy and diseases. However, it is difficult to understand the left ventricle structure intuitively because it consists of three layers with different myofiber orientations. In this work, we propose an unfolding method for micro-focus X-ray CT (µCT) volumes of the heart. First, we explore a novel supervoxel over-segmentation technique, Tensor-Based Supervoxels (TBS), which allows us to divide the left ventricle into three layers. We utilize TBS and B-spline curves for extraction of the layers. Finally we project µCT intensities in each layer to an unfolded view. Experiments are performed using three µCT images of the left ventricle acquired from canine heart specimens. In all cases, the myofiber structure could be observed clearly in the unfolded views. This is promising for helping cardiac studies.

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