Caliskan, Handenur
(2020)
Automated Bolus Detection in Videofluoroscopic Images of Swallowing Using Mask-RCNN.
Master's Thesis, University of Pittsburgh.
(Unpublished)
This is the latest version of this item.
Abstract
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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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
| Title | Member | Email Address | Pitt Username | ORCID |
|---|
| Thesis Advisor | Sejdic, Ervin | | | | | Committee Member | Akcakaya, Murat | | | | | Committee Member | Dallal, Ahmed | | | | | Committee Chair | Sejdic, 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 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/37876 |
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