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Proceedings Paper

Segmentation of intervertebral disks from videofluorographic images using convolutional neural network
Author(s): Ayano Fujinaka; Yuki Saito; Kojiro Mekata; Hotaka Takizawa; Hiroyuki Kudo
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Paper Abstract

Swallowing is achieved by a sequence of actions performed by cervical structures. Although a lot of patients suffer from dysphagia in the world, the mechanism and kinematics of swallowing are not elucidated sufficiently. This study aims to segment intervertebral disks (IDs), which are ones of representative cervical structures, in videofluorographic (VF) images by use of convolutional neural network (CNN). The proposed method consists of three steps: extraction of cervical masks, CNN-based segmentation of candidate regions of IDs, and the elimination of false positives. This segmentation method was applied to actual VF images of eleven participants that have fifty-one not-occluded IDs, and forty-three IDs were segmented successfully.

Paper Details

Date Published: 27 March 2019
PDF: 4 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110501I (27 March 2019); doi: 10.1117/12.2521249
Show Author Affiliations
Ayano Fujinaka, Univ. of Tsukuba (Japan)
Yuki Saito, Univ. of Tsukuba (Japan)
Kojiro Mekata, Univ. of Tsukuba (Japan)
Kobe Red Cross Hospital (Japan)
Hotaka Takizawa, Univ. of Tsukuba (Japan)
Hiroyuki Kudo, Univ. of Tsukuba (Japan)

Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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