Clinical evaluation of a full-image deep segmentation algorithm for the male pelvis on cone-beam CT and CT. (April 2020)

• Record Type: Journal Article
• Authors: Schreier, Jan; Genghi, Angelo; Laaksonen, Hannu; Morgas, Tomasz; Haas, Benjamin
• Is Part Of: Radiotherapy and oncology. Volume 145(2020)
• Page Start: 1
• Page End: 6
• Publication Date: 2020-04
• Subjects: Segmentation -- Deep learning -- Radiotherapy -- Male pelvis -- Artificial intelligence; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2019.11.021
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: A full-image neural network for segmentation of pelvic CT and cone-beam CT. Clinically evaluated by three clinics. Clinical acceptability equal or better than human contours. Performance better than other neural networks. Abstract: 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.