Machine learning-based detection of aberrant deep learning segmentations of target and organs at risk for prostate radiotherapy using a secondary segmentation algorithm

作者：Michaël Claessens, Verdi Vanreusel, Geert De Kerf, Isabelle Mollaert, Fredrik Löfman, Mark J. Gooding, Charlotte L. Brouwer, Piet Dirix, Dirk Verellen · 发表于：Physics in Medicine and Biology · 年份：2022 · DOI：10.1088/1361-6560/ac6fad · 被引用次数：23 · 研究领域：Advanced Radiotherapy Techniques、Advanced X-ray and CT Imaging、Radiomics and Machine Learning in Medical Imaging

Abstract Objective. The output of a deep learning (DL) auto-segmentation application should be reviewed, corrected if needed and approved before being used clinically. This verification procedure is labour-intensive, time-consuming and user-dependent, which potentially leads to significant errors with impact on the overall treatment quality. Additionally, when the time needed to correct auto-segmentations approaches the time to delineate target and organs at risk from scratch, the usability of the DL model can be questioned. Therefore, an automated quality assurance framework was developed with the aim to detect in advance aberrant auto-segmentations. Approach . Five organs (prostate, bladder, anorectum, femoral head left and right) were auto-delineated on CT acquisitions for 48 prostate patients by an in-house trained primary DL model. An experienced radiation oncologist assessed the correctness of the model output and categorised the auto-segmentations into two classes whether minor or major adaptations were needed. Subsequently, an independent, secondary DL model was implemented to delineate the same structures as the primary model. Quantitative comparison metrics were calculated using both models’ segmentations and used as input features for a machine learning classification model to predict the output quality of the primary model. Main results . For every organ, the approach of independent validation by the secondary model was able to detect primary auto-segmentations th...