arXiv preprint arXiv:1811.02629 (2018)īaumgartner, C.F., et al.: PHiSeg: capturing uncertainty in medical image segmentation. Data 4, 1–13 (2017)īakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. 286, (2017)īakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. īakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. The results demonstrate that SVLS, despite its simplicity, obtains superior boundary prediction with improved uncertainty and model calibration.Īrmato III, S.G., et al.: Data from LIDC-IDRI. The proposed approach is extensively validated on four clinical segmentation tasks with different imaging modalities, number of classes and single and multi-rater expert annotations. SVLS also naturally lends itself to incorporate inter-rater uncertainty when multiple labelmaps are available. Here, we propose Spatially Varying Label Smoothing (SVLS), a soft labeling technique that captures the structural uncertainty in semantic segmentation. However, LS is not taking the local structure into account and results in overly smoothed predictions with low confidence even for non-ambiguous regions. We built upon label smoothing (LS) where a network is trained on ‘blurred’ versions of the ground truth labels which has been shown to be effective for calibrating output predictions. We argue that this information can be extracted from the expert annotations at no extra cost, and when integrated into state-of-the-art neural networks, it can lead to improved calibration between soft probabilistic predictions and the underlying uncertainty. The task of image segmentation is inherently noisy due to ambiguities regarding the exact location of boundaries between anatomical structures.
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