Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/43615
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DC Field | Value | Language |
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dc.contributor.advisor | Reniers, Brigitte | - |
dc.contributor.advisor | Crijns, Wouter | - |
dc.contributor.author | CAVUS, Hasan | - |
dc.contributor.author | Bulens, Philippe | - |
dc.contributor.author | Tournel, Koen | - |
dc.contributor.author | Orlandini, Marc | - |
dc.contributor.author | Jankelevitch, Alexandra | - |
dc.contributor.author | Crijns, Wouter | - |
dc.contributor.author | RENIERS, Brigitte | - |
dc.date.accessioned | 2024-08-29T13:58:39Z | - |
dc.date.available | 2024-08-29T13:58:39Z | - |
dc.date.issued | 2024 | - |
dc.date.submitted | 2024-08-20T11:08:36Z | - |
dc.identifier.citation | Physics & Imaging in Radiation Oncology, 31 (Art N° 100627) | - |
dc.identifier.uri | http://hdl.handle.net/1942/43615 | - |
dc.description.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. | - |
dc.language.iso | en | - |
dc.publisher | Elsevier B.V. | - |
dc.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/). | - |
dc.subject.other | Automation | - |
dc.subject.other | Deep-Learning | - |
dc.subject.other | ESAPI | - |
dc.subject.other | Segmentation | - |
dc.title | Safety and efficiency of a fully automatic workflow for auto-segmentation in radiotherapy using three commercially available deep learning-based applications | - |
dc.type | Journal Contribution | - |
dc.identifier.volume | 31 | - |
local.bibliographicCitation.jcat | A1 | - |
local.publisher.place | Radarweg 29 1043 NX Amsterdam Netherlands | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.bibliographicCitation.artnr | 100627 | - |
dc.identifier.doi | 10.1016/j.phro.2024.100627 | - |
local.provider.type | - | |
local.uhasselt.international | no | - |
item.fullcitation | CAVUS, Hasan; Bulens, Philippe; Tournel, Koen; Orlandini, Marc; Jankelevitch, Alexandra; Crijns, Wouter & RENIERS, Brigitte (2024) Safety and efficiency of a fully automatic workflow for auto-segmentation in radiotherapy using three commercially available deep learning-based applications. In: Physics & Imaging in Radiation Oncology, 31 (Art N° 100627). | - |
item.accessRights | Open Access | - |
item.fulltext | With Fulltext | - |
item.contributor | CAVUS, Hasan | - |
item.contributor | Bulens, Philippe | - |
item.contributor | Tournel, Koen | - |
item.contributor | Orlandini, Marc | - |
item.contributor | Jankelevitch, Alexandra | - |
item.contributor | Crijns, Wouter | - |
item.contributor | RENIERS, Brigitte | - |
crisitem.journal.eissn | 2405-6316 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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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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