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Human ratings are abstract representations of segmentation quality. Toapproximate human quality ratings on scarce expert data, we train surrogatequality estimation models. We evaluate on a complex multi-class segmentationproblem, specifically glioma segmentation, following the BraTS annotationprotocol. The training data features quality ratings from 15 expertneuroradiologists on a scale ranging from 1 to 6 stars for variouscomputer-generated and manual 3D annotations. Even though the networks operateon 2D images and with scarce training data, we can approximate segmentationquality within a margin of error comparable to human intra-rater reliability.Segmentation quality prediction has broad applications. While an understandingof segmentation quality is imperative for successful clinical translation ofautomatic segmentation quality algorithms, it can play an essential role intraining new segmentation models. Due to the split-second inference times, itcan be directly applied within a loss function or as a fully-automatic datasetcuration mechanism in a federated learning setting.

Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings