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Clinical evaluation of a full-image deep segmentation algorithm for the male pelvis on cone-beam CT and CT
Radiotherapy and Oncology ( IF 5.7 ) Pub Date : 2020-04-01 , DOI: 10.1016/j.radonc.2019.11.021
Jan Schreier 1 , Angelo Genghi 1 , Hannu Laaksonen 1 , Tomasz Morgas 1 , Benjamin Haas 1
Affiliation  

AIM The segmentation of organs from a CT scan is a time-consuming task, which is one hindrance for adaptive radiation therapy. Through deep learning, it is possible to automatically delineate organs. Metrics like dice score do not necessarily represent the impact for clinical practice. Therefore, a clinical evaluation of the deep neural network is needed to verify the segmentation quality. METHODS In this work, a novel deep neural network is trained on 300 CT and 300 artificially generated pseudo CBCTs to segment bladder, prostate, rectum and seminal vesicles from CT and cone beam CT scans. The model is evaluated on 45 CBCT and 5 CT scans through a clinical review performed by three different clinics located in Europe, North America and Australia. RESULTS The deep learning model is scored either equally good (prostate and seminal vesicles) or better (bladder and rectum) than the structures from routine clinical practice. No or minor corrections are required for 97.5% of the segmentations of the bladder, 91.5% of the prostate, 94% of the rectum and seminal vesicles. Overall, for 82.5% of the patients none of the organs need major corrections or a redraw. CONCLUSION This study shows that modern deep neural networks are capable of producing clinically applicable organ segmentation for the male pelvis. The model is able to produce acceptable structures as frequently as current clinical routine. Therefore, deep neural networks can simplify the clinical workflow by offering initial segmentations. The study further shows that to retain the clinicians' personal preferences a structure review and correction is necessary for structures both created by other clinicians and deep neural networks.

中文翻译:

锥形束CT和CT对男性骨盆全图像深度分割算法的临床评价

目的 从 CT 扫描中分割器官是一项耗时的任务,这是自适应放射治疗的一个障碍。通过深度学习,可以自动勾画器官。骰子分数等指标不一定代表对临床实践的影响。因此,需要对深度神经网络进行临床评估以验证分割质量。方法在这项工作中,一个新的深度神经网络在 300 个 CT 和 300 个人工生成的伪 CBCT 上进行训练,以从 CT 和锥形束 CT 扫描中分割膀胱、前列腺、直肠和精囊。该模型通过位于欧洲、北美和澳大利亚的三个不同诊所进行的临床审查在 45 次 CBCT 和 5 次 CT 扫描上进行评估。结果 深度学习模型的评分与常规临床实践中的结构相同(前列腺和精囊)或更好(膀胱和直肠)。97.5% 的膀胱分割、91.5% 的前列腺、94% 的直肠和精囊分割不需要或只需要少量修正。总体而言,对于 82.5% 的患者来说,没有一个器官需要大的矫正或重新抽取。结论 本研究表明,现代深度神经网络能够为男性骨盆生成临床适用的器官分割。该模型能够像当前临床常规一样频繁地产生可接受的结构。因此,深度神经网络可以通过提供初始分割来简化临床工作流程。该研究进一步表明,为了保留临床医生的
更新日期:2020-04-01
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