Zusammenfassung
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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Literatur
Sudkamp H, Koch P, Spahr H et al. In-vivo retinal imaging with off-axis full-field timedomain optical coherence tomography. Opt Lett. 2016;41(21):4987–90.
Bogunovic H, Venhuizen F, Klimscha S et al. RETOUCH-The retinal OCT fluid detection and segmentation benchmark and challenge. IEEE Trans Med Imaging. 2019;38:1858–74.
Kepp T, Sudkamp H, von der Burchard C et al. Segmentation of retinal low-cost optical coherence tomography images using deep learning. Medical Imaging 2020: Computer-Aided Diagnosis. Vol. 11314. SPIE, 2020:113141O.
Joy TT, Sedai S, Garnavi R. Analyzing epistemic and aleatoric uncertainty for drusen segmentation in optical coherence tomography images. AIII Workshop on Trustworthy AI for Healthcare. 2021.
Kendall A, Gal Y. What uncertainties do we need in bayesian deep learning for computer vision? Advances in Neural Information Processing Systems. Vol. 30. 2017:5580–90.
Wang G, Li W, Aertsen M et al. Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing. 2019;338:34–45.
Kepp T, Droigk C, Casper M et al. Segmentation of mouse skin layers in optical coherence tomography image data using deep convolutional neural networks. Biomed Opt Express. 2019;10(7):3484–96.
Mehta R, Filos A, Gal Y, Arbel T. Uncertainty evaluation metric for brain tumour segmentation. Medical Imaging with Deep Learning (MIDL). 2020.
Roy AG, Conjeti S, Navab N, Wachinger C. Bayesian QuickNAT: model uncertainty in deep whole-brain segmentation for structure-wise quality control. NeuroImage. 2019;195:11–22.
von der Burchard C, Moltmann M, Tode J et al. Self-examination low-cost full-field OCT (SELFF-OCT) for patients with various macular diseases. Graefes Arch Clin Exp Ophthalmol. 2021;259(6):1503–11.
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© 2022 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Kepp, T. et al. (2022). Epistemic and Aleatoric Uncertainty Estimation for PED, Segmentation in Home OCT Images. In: Maier-Hein, K., Deserno, T.M., Handels, H., Maier, A., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2022. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-36932-3_7
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DOI: https://doi.org/10.1007/978-3-658-36932-3_7
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