Label-set impact on deep learning-based prostate segmentation on MRI

ConclusionsWe investigated the impact of label-set selection on the performance of a DL-based prostate segmentation model. We found that the use of different sets of manual prostate gland and zone segmentations has a measurable impact on model performance. Nevertheless, DL-based segmentation appeared to have a greater inter-reader agreement than manual segmentation. More thought should be given to the label-set, with a focus on multicenter manual segmentation and agreement on common procedures.Critical relevance statementLabel-set selection significantly impacts the performance of a deep learning-based prostate segmentation model. Models using different label-set showed higher agreement than manual segmentations.Key points• Label-set selection has a significant impact on the performance of automatic segmentation models.• Deep learning-based models demonstrated true learning rather than simply mimicking the label-set.• Automatic segmentation appears to have a greater inter-reader agreement than manual segmentation.Graphical Abstract
Source: Insights into Imaging - Category: Radiology Source Type: research