Ross, T;
Tanna, R;
Lilaonitkul, W;
Mehta, N;
(2024)
Deep Learning for Automated Image Segmentation of the Middle Ear: A Scoping Review.
Otolaryngology - Head and Neck Surgery
10.1002/ohn.758.
(In press).
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Abstract
Objective: Convolutional neural networks (CNNs) have revolutionized medical image segmentation in recent years. This scoping review aimed to carry out a comprehensive review of the literature describing automated image segmentation of the middle ear using CNNs from computed tomography (CT) scans. Data Sources: A comprehensive literature search, generated jointly with a medical librarian, was performed on Medline, Embase, Scopus, Web of Science, and Cochrane, using Medical Subject Heading terms and keywords. Databases were searched from inception to July 2023. Reference lists of included papers were also screened. Review Methods: Ten studies were included for analysis, which contained a total of 866 scans which were used in model training/testing. Thirteen different architectures were described to perform automated segmentation. The best Dice similarity coefficient (DSC) for the entire ossicular chain was 0.87 using ResNet. The highest DSC for any structure was the incus using 3D-V-Net at 0.93. The most difficult structure to segment was the stapes, with the highest DSC of 0.84 using 3D-V-Net. Conclusions: Numerous architectures have demonstrated good performance in segmenting the middle ear using CNNs. To overcome some of the difficulties in segmenting the stapes, we recommend the development of an architecture trained on cone beam CTs to provide improved spatial resolution to assist with delineating the smallest ossicle. Implications for Practice: This has clinical applications for preoperative planning, diagnosis, and simulation.
| Type: | Article |
|---|---|
| Title: | Deep Learning for Automated Image Segmentation of the Middle Ear: A Scoping Review |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/ohn.758 |
| Publisher version: | http://dx.doi.org/10.1002/ohn.758 |
| Language: | English |
| Additional information: | © 2024 The Authors. Otolaryngology–Head and Neck Surgery published by Wiley Periodicals LLC on behalf of American Academy of Otolaryngology–Head and Neck Surgery Foundation. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | artificial intelligence, convolutional neural networks, image segmentation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10192418 |
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