Tomography · Published 2026-07-29 · DOI 10.3390/tomography12080110
Jingmin Luan, Tian Jiang, Xiaohao Sun, Jian Liu, Yao Yu, Xiaojiao Dong, Yang Wang, Yajie Zhang, Zhenhe Ma
<b>Background:</b> Ischemia-related optical coherence tomography angiography (OCTA) signal degradation complicates cerebral vessel segmentation and quantitative analysis. We developed a structure-aware segmentation framework and applied it to exploratory longitudinal vascular quantification. <b>Methods</b>: Six adult male C57BL/6 mice were imaged at baseline and 0.5–10 h after photothrombotic induction. After geometric transformation, 345 image instances were separated by animal into training, validation, and test subsets of 240, 30, and 75 images from four, one, and one mouse; the 75 test instances originated from 25 source images. A separate sixfold subject-level cross-validation generated out-of-sample masks for longitudinal analysis. <b>Results</b>: The proposed method achieved a Dice coefficient of 0.887, a centerline Dice (clDice) of 0.953, a branch-point F1 score of 0.739, and an average surface distance (ASD) of 0.564 pixels, with significant improvements in Dice, clDice, and branch-point F1. Vascular perfusion density (VPD) and vessel length (VL) decreased significantly over time, vessel average diameter (VAD) remained stable, and average curvature (AC) and branch-point density (BPD) showed significant overall temporal effects. <b>Conclusions</b>: The framework improved technical segmentation performance and supported exploratory analysis of OCTA-derived vascular measurements in experimental ischemic stroke.
Abstract from DOAJ. Public domain (CC0 1.0).
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Luan, J., Jiang, T., Sun, X., et al. (2026). Structure-Aware OCTA Segmentation for OCTA-Derived Quantitative Analysis of Cerebral Microvascular Perfusion in Ischemic Stroke. Tomography. https://doi.org/10.3390/tomography12080110