Detection of the pharyngeal phase in the videofluoroscopic swallowing study using inflated 3d convolutional networks

Jong Taek Lee; Eunhee Park

doi:10.1007/978-3-030-00919-9_38

Detection of the pharyngeal phase in the videofluoroscopic swallowing study using inflated 3d convolutional networks

Jong Taek Lee, Eunhee Park

Kyungpook National University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Videofluoroscopic swallowing study (VFSS) is a standard diagnostic tool for dysphagia. Previous computer assisted analysis of VFSS required manual preparation to mark several anatomical structures and to select time intervals of interest such as a pharyngeal phase during swallowing. These processes were still costly and challenging for clinicians. In this study, we present a novel approach to detect the pharyngeal phase of swallowing through whole of VFSS video clips using Inflated 3D Convolutional Networks (I3D) without additional manual annotations.

Original language	English
Title of host publication	Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings
Editors	Mingxia Liu, Heung-Il Suk, Yinghuan Shi
Publisher	Springer Verlag
Pages	328-336
Number of pages	9
ISBN (Print)	9783030009182
DOIs	https://doi.org/10.1007/978-3-030-00919-9_38
State	Published - 2018
Event	9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 16 Sep 2018 → 16 Sep 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11046 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	16/09/18 → 16/09/18

Keywords

Action classification
Dysphagia
Inflated 3D convolutional networks
Videofluoroscopic swallowing study

Access to Document

10.1007/978-3-030-00919-9_38

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Lee, J. T., & Park, E. (2018). Detection of the pharyngeal phase in the videofluoroscopic swallowing study using inflated 3d convolutional networks. In M. Liu, H.-I. Suk, & Y. Shi (Eds.), Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings (pp. 328-336). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11046 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00919-9_38

Lee, Jong Taek ; Park, Eunhee. / Detection of the pharyngeal phase in the videofluoroscopic swallowing study using inflated 3d convolutional networks. Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. editor / Mingxia Liu ; Heung-Il Suk ; Yinghuan Shi. Springer Verlag, 2018. pp. 328-336 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Detection of the pharyngeal phase in the videofluoroscopic swallowing study using inflated 3d convolutional networks",

abstract = "Videofluoroscopic swallowing study (VFSS) is a standard diagnostic tool for dysphagia. Previous computer assisted analysis of VFSS required manual preparation to mark several anatomical structures and to select time intervals of interest such as a pharyngeal phase during swallowing. These processes were still costly and challenging for clinicians. In this study, we present a novel approach to detect the pharyngeal phase of swallowing through whole of VFSS video clips using Inflated 3D Convolutional Networks (I3D) without additional manual annotations.",

keywords = "Action classification, Dysphagia, Inflated 3D convolutional networks, Videofluoroscopic swallowing study",

author = "Lee, {Jong Taek} and Eunhee Park",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 ; Conference date: 16-09-2018 Through 16-09-2018",

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doi = "10.1007/978-3-030-00919-9_38",

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Lee, JT & Park, E 2018, Detection of the pharyngeal phase in the videofluoroscopic swallowing study using inflated 3d convolutional networks. in M Liu, H-I Suk & Y Shi (eds), Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11046 LNCS, Springer Verlag, pp. 328-336, 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, Granada, Spain, 16/09/18. https://doi.org/10.1007/978-3-030-00919-9_38

Detection of the pharyngeal phase in the videofluoroscopic swallowing study using inflated 3d convolutional networks. / Lee, Jong Taek; Park, Eunhee.
Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. ed. / Mingxia Liu; Heung-Il Suk; Yinghuan Shi. Springer Verlag, 2018. p. 328-336 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11046 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Videofluoroscopic swallowing study (VFSS) is a standard diagnostic tool for dysphagia. Previous computer assisted analysis of VFSS required manual preparation to mark several anatomical structures and to select time intervals of interest such as a pharyngeal phase during swallowing. These processes were still costly and challenging for clinicians. In this study, we present a novel approach to detect the pharyngeal phase of swallowing through whole of VFSS video clips using Inflated 3D Convolutional Networks (I3D) without additional manual annotations.

AB - Videofluoroscopic swallowing study (VFSS) is a standard diagnostic tool for dysphagia. Previous computer assisted analysis of VFSS required manual preparation to mark several anatomical structures and to select time intervals of interest such as a pharyngeal phase during swallowing. These processes were still costly and challenging for clinicians. In this study, we present a novel approach to detect the pharyngeal phase of swallowing through whole of VFSS video clips using Inflated 3D Convolutional Networks (I3D) without additional manual annotations.

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Lee JT, Park E. Detection of the pharyngeal phase in the videofluoroscopic swallowing study using inflated 3d convolutional networks. In Liu M, Suk HI, Shi Y, editors, Machine Learning in Medical Imaging - 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. Springer Verlag. 2018. p. 328-336. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-00919-9_38

Detection of the pharyngeal phase in the videofluoroscopic swallowing study using inflated 3d convolutional networks

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Keywords

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