Quantifying and visualising uncertainty in deep learning-based segmentation for radiation therapy treatment planning: What do radiation oncologists and therapists want?
Autor: | Huet-Dastarac M; Molecular Imaging, Radiation and Oncology lab (MIRO), UCLouvain, Brussels, Belgium. Electronic address: margerie.huet@uclouvain.be., van Acht NMC; Catharina Hospital Eindhoven - department of radiation oncology, Eindhoven, The Netherlands; Eindhoven University of Technology - Department of Electrical Engineering and Department of Applied Physics and Science Education, Eindhoven, The Netherlands., Maruccio FC; The Netherlands Cancer Institute (NKI), Department of Radiation Oncology, Amsterdam, The Netherlands., van Aalst JE; University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands; University of Twente, Department of Technical Medicine, Enschede, The Netherlands., van Oorschodt JCJ; Catharina Hospital Eindhoven - department of radiation oncology, Eindhoven, The Netherlands; Eindhoven University of Technology - Department of Electrical Engineering and Department of Applied Physics and Science Education, Eindhoven, The Netherlands., Cnossen F; University of Groningen, Department of Artificial Intelligence, Groningen, The Netherlands., Janssen TM; The Netherlands Cancer Institute (NKI), Department of Radiation Oncology, Amsterdam, The Netherlands., Brouwer CL; University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands., Barragan Montero A; Molecular Imaging, Radiation and Oncology lab (MIRO), UCLouvain, Brussels, Belgium., Hurkmans CW; Catharina Hospital Eindhoven - department of radiation oncology, Eindhoven, The Netherlands; Eindhoven University of Technology - Department of Electrical Engineering and Department of Applied Physics and Science Education, Eindhoven, The Netherlands. |
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Jazyk: | angličtina |
Zdroj: | Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology [Radiother Oncol] 2024 Dec; Vol. 201, pp. 110545. Date of Electronic Publication: 2024 Sep 24. |
DOI: | 10.1016/j.radonc.2024.110545 |
Abstrakt: | Background and Purpose: During the ESTRO 2023 physics workshop on "AI for the fully automated radiotherapy treatment chain", the topic of deep learning (DL) segmentation was discussed. Despite its widespread use in radiotherapy, the time needed to evaluate and correct DL segmentations remains burdensome. While segmentation uncertainty could be beneficial for clinicians, there is a lack of understanding on what information should be presented to ease their task. This study aimed to gather insights from clinicians on uncertainty visualisation options. Materials and Methods: Two sessions of structured interviews were conducted across four institutions already using DL segmentation clinically. The first session focused on the main problems hindering the clinical use of DL. In the second session, ten visualisation options displaying uncertainty information at different levels (structure, slice, or voxel) with binary or continuous values were presented. Dosimetric information was also present in some visualisations. For each case, sixteen clinicians (radiation oncologists and radiation therapists) were asked to grade an overall score, the usability, the training required, and the expected time gain. Results: Participants preferred the binary voxel-level uncertainty visualisation, followed by binary structure-level uncertainty visualisation. Combining structure-level and voxel-level visualisation methods has been proposed as a promising approach. The benefits of dosimetric information were perceived diversely among participants since it complexifies the display but could be useful for the online adaptive workflow. Conclusion: Preferences for uncertainty visualisation methods were assessed within a multi-institutional experienced group of clinicians. Further refinement of preferences may help in selecting the best options for clinical implementation. Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2024 Elsevier B.V. All rights reserved.) |
Databáze: | MEDLINE |
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