AI in Medicine

Evidential Deep Learning for Missing Boundary Detection in Topologically Constrained OCT Layer Segmentation

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Abstract

Optical coherence tomography (OCT) layer boundary regression methods provide sub-pixel precision and topological guarantees but fundamentally assume that every layer exists across all A-scans. This mathematical constraint fails in severe pathologies such as Geographic Atrophy (GA), where specific retinal layers disappear. We extend the topologically constrained SD-RetinaNet framework to jointly perform boundary regression and explicitly detect missing layers using uncertainty quantification. We introduce a Gaussian Negative Log-Likelihood (NLL) formulation to calibrate aleatoric uncertainty, capturing spatial boundary errors. Concurrently, we employ an Evidential Deep Learning (EDL) module to model epistemic uncertainty directly from the network outputs, allowing the network to detect regions with zero structural evidence for a layer. Our framework addresses the largely overlooked challenge of anatomical absence in boundary regression, combining sub-pixel localization with direct atrophy segmentation.

How to Cite:

Fazekas, B. & Bogunovic, H., (2026) “Evidential Deep Learning for Missing Boundary Detection in Topologically Constrained OCT Layer Segmentation”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision 3(1), 17-22.