Σφακιανάκης Αλέξανδρος
ΩτοΡινοΛαρυγγολόγος
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Κρήτη 72100
00302841026182
00306932607174
alsfakia@gmail.com

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Δευτέρα 26 Οκτωβρίου 2020

Automated Bolus Detection in Videofluoroscopic Images of Swallowing Using Mask-RCNN.

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Automated Bolus Detection in Videofluoroscopic Images of Swallowing Using Mask-RCNN.

Annu Int Conf IEEE Eng Med Biol Soc. 2020 07;2020:2173-2177

Authors: Caliskan H, Mahoney AS, Coyle JL, Sejdic E

Abstract
Tracking a liquid or food bolus in videofluoroscopic images during X-ray based diagnostic swallowing examinations is a dominant clinical approach to assess human swallowing function during oral, pharyngeal and esophageal stages of swallowing. This tracking represents a highly challenging problem for clinicians as swallowing is a rapid action. Therefore, we developed a computer-aided method to automate bolus detection and tracking in order to alleviate issues associated with human factors. Specifically, we applied a stateof-the-art deep learning model called Mask-RCNN to detect and segment the bolus in videofluoroscopic image sequences. We trained the algorithm with 450 swallow videos and evaluated with an independent dataset of 50 videos. The algorithm was able to detect and segment the bolus with a mean average precision of 0.49 and an intersection of union of 0.71. The proposed method indicated robust detection results that can help to improve the speed and accuracy of a cl inical decisionmaking process.

PMID: 33018437 [PubMed - indexed for MEDLINE]

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