Abstract
Accurately assessing the collateral circulation is critically essential for making acute ischemic stroke treatment plans. Current automatic methods usually rely on a single-stage CNN classifier, which typically requires a huge amount of data for training and thus struggles to cope with the challenge of limited data in clinical practice. To achieve an objective and efficient collateral circulation assessment under small datasets, we propose a two-stage automatic collateral scoring framework composed of a brain vessel segmentation and a scoring classifier. In the segmentation stage, we introduce an improved U-Net named BVU-Net, which can address the diverse and scattered brain vessel morphology in CTA images and achieve more precise segmentation results. In the assessment stage, we propose the Seg-based Vessel Indicator Set (SVIS), comprising four vessel quantification indicators extracted from the output masks of BVU-Net. Using SVIS, classifiers are evaluated on a small clinical dataset of 191 patients. The experiment results demonstrate that the proposed framework significantly outperforms single-stage CNN classifiers, showing substantial advantages and providing a valuable reference for clinical decision-making.
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This work was supported in part by the National Natural Science Foundation of China (grant number 61501297).
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Zhang, T., Huang, H., Ma, Y., Huang, B., Lu, W., Xu, A. (2025). A Two-Stage Automatic Collateral Scoring Framework Based on Brain Vessel Segmentation. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15044. Springer, Singapore. https://doi.org/10.1007/978-981-97-8496-7_29
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DOI: https://doi.org/10.1007/978-981-97-8496-7_29
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