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

Caliskan, Handenur (2020) Automated Bolus Detection in Videofluoroscopic Images of Swallowing Using Mask-RCNN. Master's Thesis, University of Pittsburgh. (Unpublished)

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Tracking food bolus in videofluoroscopic images during swallowing 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 proposed a state-of-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 clinical decision-making process.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorSejdic, Ervin
Committee MemberAkcakaya, Murat
Committee MemberDallal, Ahmed
Committee ChairSejdic, Ervin
Date: 29 July 2020
Date Type: Publication
Defense Date: 14 November 2019
Approval Date: 29 July 2020
Submission Date: 6 November 2019
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 63
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Deep learning, swallowing difficulties,image segmentation, object detection, Mask-RCNN, X-ray
Date Deposited: 29 Jul 2020 13:10
Last Modified: 29 Jul 2022 05:15

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