使用解剖学极端病例对骨盆自动分割算法进行压力测试。
Stress-testing pelvic autosegmentation algorithms using anatomical edge cases.
作者信息
Kanwar Aasheesh, Merz Brandon, Claunch Cheryl, Rana Shushan, Hung Arthur, Thompson Reid F
机构信息
Department of Radiation Medicine, Oregon Health and Sciences University, Portland, OR, United States.
Department of Radiation Oncology, Baylor College of Medicine, Houston, TX, United States.
出版信息
Phys Imaging Radiat Oncol. 2023 Jan 16;25:100413. doi: 10.1016/j.phro.2023.100413. eCollection 2023 Jan.
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.
商业自动分割技术已进入临床应用，然而在某些情况下，其在实际应用中的性能可能会受到影响。我们旨在评估解剖变异对性能的影响。我们识别出112例具有解剖变异的前列腺癌患者（边缘病例）。使用三种商业工具对盆腔解剖结构进行自动分割。为了评估性能，计算了与临床医生划定的参考标准相比的骰子相似系数、平均表面距离和95%豪斯多夫距离。深度学习自动分割优于基于图谱和基于模型的方法。然而，与正常队列相比，边缘病例的性能较低（平均骰子相似系数降低0.12）。解剖变异给商业自动分割带来了挑战。
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