Abstract
Robust delineation of retinal microvasculature in optical coherence tomography angiography (OCTA) images remains a challenging task, particularly in handling the weak continuity of vessels, low visibility of capillaries, and significant noise interferences. This paper introduces a modulatory elongated model to overcome these difficulties by exploiting the facilitatory and inhibitory interactions exhibited by the contextual influences for neurons in the primary visual cortex. We construct the receptive field of the neurons by an elongated representation, which encodes the underlying profile of vasculature structures, elongated-like patterns, in an anisotropic neighborhood. An annular function is formed to capture the contextual influences presented in the surrounding region outside the neuron support and provide an automatic tuning of contextual information. The proposed modulatory method incorporates the elongated responses with the contextual influences to produce spatial coherent responses for delineating microvasculature features more distinctively from their background regions. Experimental evaluation on clinical retinal OCTA images shows the effectiveness of the proposed model in attaining a promising performance, outperforming the state-of-the-art vessel delineation methods.
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Challoob, M., Gao, Y., Busch, A., Zhang, W. (2023). A Modulatory Elongated Model for Delineating Retinal Microvasculature in OCTA Images. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_67
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