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Epistemic and Aleatoric Uncertainty Estimation for PED, Segmentation in Home OCT Images

Timo Kepp; Julia Andresen; Helge Sudkamp; Claus von der Burchard; Johann Roider; Gereon Hüttmann; Jan Ehrhardt; Heinz Handels
In: Klaus Maier-Hein; Thomas M Deserno; Heinz Handels; Andreas Maier; Christoph Palm; Thomas Tolxdorff (Hrsg.). Bildverarbeitung für die Medizin 2022. Pages 32-37, Springer, 2022.


Newinnovative low-cost optical coherence tomography (OCT) devices enable flexible monitoring of age-related macular degeneration (AMD) at home. In combination with current machine learning algorithms like convolutional neural networks (CNNs), assessment of AMD-related biomarkers such as pigment epithelial detachment (PED) can be supported by automatic segmentation. However, limited availability of medical image data as well as noisy ground truth does not guarantee a high generalizability of CNN models. Estimating a segmentationrelated uncertainty can be used to evaluate the confidence of the prediction. In this work, two types of uncertainties are analyzed for the segmentation of PED in home OCT image data. Epistemic and aleatoric uncertainties are determined by dropout and augmentation at test time, respectively. Evaluations are performed using pixel-wise as well as structure-wise uncertainty metrics. Results show that test-time augmentation produces both more accurate segmentations and more reliable uncertainties.

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