Stress-testing pelvic autosegmentation algorithms using anatomical edge cases.
| Autor: | Kanwar A; Department of Radiation Medicine, Oregon Health and Sciences University, Portland, OR, United States., Merz B; Department of Radiation Medicine, Oregon Health and Sciences University, Portland, OR, United States., Claunch C; Department of Radiation Oncology, Baylor College of Medicine, Houston, TX, United States., Rana S; PeaceHealth Medical Group - PeaceHealth Southwest Radiation Oncology, Vancouver, Washington, United States., Hung A; Department of Radiation Medicine, Oregon Health and Sciences University, Portland, OR, United States., Thompson RF; Department of Radiation Medicine, Oregon Health and Sciences University, Portland, OR, United States.; Division of Hospital and Specialty Medicine, VA Portland Healthcare System, Portland, OR, United States. |
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| Jazyk: | angličtina |
| Zdroj: | Physics and imaging in radiation oncology [Phys Imaging Radiat Oncol] 2023 Jan 16; Vol. 25, pp. 100413. Date of Electronic Publication: 2023 Jan 16 (Print Publication: 2023). |
| DOI: | 10.1016/j.phro.2023.100413 |
| Abstrakt: | Commercial autosegmentation has entered clinical use, however real-world performance may suffer in certain cases. We aimed to assess the influence of anatomic variants on performance. We identified 112 prostate cancer patients with anatomic variations (edge cases). Pelvic anatomy was autosegmented using three commercial tools. To evaluate performance, Dice similarity coefficients, and mean surface and 95% Hausdorff distances were calculated versus clinician-delineated references. Deep learning autosegmentation outperformed atlas-based and model-based methods. However, edge case performance was lower versus the normal cohort (0.12 mean DSC reduction). Anatomic variation presents challenges to commercial autosegmentation. Competing Interests: 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. |
| Databáze: | MEDLINE |
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