ArticleNeuroradiology2026
Reconstruction of complete cerebral arterial anatomy from non-contrast CT using deep learning for pre-thrombectomy guidance.
Article in Neuroradiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
background and purposeTraditional imaging methods, like CT angiography (CTA), are unable to visualize the occluded vessels in acute ischemic stroke (AIS). We aimed to develop a deep learning based segmentation model for reconstructing the complete cerebral vasculature from non-contrast CT (NCCT) in LVO-AIS patients for endovascular thrombectomy planning.
methodsA nnU-Net model was trained and validated on retrospectively collected paired NCCT-CTA head images without large vessel occlusions (LVOs) from December 2018 to July 2025 (dataset 1: n = 280, for model training, internal validation and internal test; dataset 2: n = 40, for external validation). Model performance was evaluated using quantitative segmentation metrics, including the Dice Similarity Coefficient (DSC). Additionally, the model was evaluated on NCCT images of LVO-AIS patients from two hospitals, and the segmentation results were verified against post-recanalization DSA by two radiologists (dataset 3: n = 290).
resultsThe nnU-Net model demonstrated robust segmentation performance, achieving DSC of 0.80 ± 0.04, 0.79 ± 0.04, and 0.79 ± 0.04 on the internal validation, internal test, and external validation sets, respectively. In the clinical evaluation involving LVO-AIS patients, high-quality segmentations were assigned by Rater 1 in 98.9% of cases and by Rater 2 in 98.3% of cases, with substantial inter-rater agreement (Cohen's κ = 0.74; 95% CI, 0.56-0.89).
conclusionThis study demonstrated the feasibility of using a deep learning model to segment cerebral vasculature from NCCT images in LVO-AIS patients. The proposed approach may provide complementary anatomical information for thrombectomy planning, but prospective studies are needed to determine its clinical impact.
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