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http://hdl.handle.net/1942/43615
Title: | Safety and efficiency of a fully automatic workflow for auto-segmentation in radiotherapy using three commercially available deep learning-based applications | Authors: | CAVUS, Hasan Bulens, Philippe Tournel, Koen Orlandini, Marc Jankelevitch, Alexandra Crijns, Wouter RENIERS, Brigitte |
Advisors: | Reniers, Brigitte Crijns, Wouter |
Issue Date: | 2024 | Publisher: | Elsevier B.V. | Source: | Physics & Imaging in Radiation Oncology, 31 (Art N° 100627) | Abstract: | Advancements in radiotherapy auto-segmentation necessitate reliable and efficient workflows. Therefore, a standardized fully automatic workflow was developed for three commercially available deep learning-based auto-segmentation applications and compared to a manual workflow for safety and efficiency. The workflow underwent safety evaluation with failure mode and effects analysis. Notably, eight failure modes were reduced, including seven with severity factors ≥7, indicating the effect on patients, and two with Risk Priority Number value >125, which assesses relative risk level. Efficiency, measured by mouse clicks, showed zero clicks with the automatic workflow. This automation illustrated improvement in both safety and efficiency of workflow. | Keywords: | Automation;Deep-Learning;ESAPI;Segmentation | Document URI: | http://hdl.handle.net/1942/43615 | e-ISSN: | 2405-6316 | DOI: | 10.1016/j.phro.2024.100627 | Rights: | 2024 The Author(s). Published by Elsevier B.V. on behalf of European Society of Radiotherapy & Oncology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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Safety and efficiency of a fully automatic workflow for auto-segmentation.pdf | Published version | 508.12 kB | Adobe PDF | View/Open |
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