Rethinking the neighborhood information for deep learning-based optical coherence tomography angiography.
Journal:
Medical physics
Published Date:
Jun 1, 2022
Abstract
PURPOSE: Optical coherence tomography angiography (OCTA) is a premium imaging modality for noninvasive microvasculature studies. Deep learning networks have achieved promising results in the OCTA reconstruction task, benefiting from their powerful modeling capability. However, two limitations exist in the current deep learning-based OCTA reconstruction methods: (a) the angiogram information extraction is only limited to the locally consecutive B-scans; and (b) all reconstruction models are confined to the 2D convolutional network architectures, lacking effective temporal modeling. As a result, the valuable neighborhood information and inherent temporal characteristics of OCTA are not fully utilized. In this paper, we designed a neighborhood information-fused Pseudo-3D U-Net (NI-P3D-U) for OCTA reconstruction.