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M.A. Leenders
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Master thesis
(2026)
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M.A. Leenders, F.M. Vos, T. van Walsum, F.G. te Nijenhuis, R. Su, D.M.J. Tax, F.J.H. Gijsen
Digital subtraction angiography (DSA) is widely used to assess vascular changes during endovascular thrombectomy, but manual comparison of image pairs is challenging and subjective. This work presents a deep learning-based method for arterial change detection in cerebral DSA by combining inter-sequence registration with a siamese spatiotemporal U-Net for joint arterial segmentation and change detection. Experiments on data from the MR CLEAN registry demonstrate that the proposed registration method significantly improves accuracy compared with the best-performing baseline (p=0.0054) and that the change-detection network consistently identifies arterial changes in successfully co-registered DSA pairs (Dice = 0.70). A preliminary reader study indicated that marking these changes can improve inter-rater agreement of extended thrombolysis in cerebral infarction (eTICI) scoring and increase the detection rate of emboli in new territory (ENT).
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Digital subtraction angiography (DSA) is widely used to assess vascular changes during endovascular thrombectomy, but manual comparison of image pairs is challenging and subjective. This work presents a deep learning-based method for arterial change detection in cerebral DSA by combining inter-sequence registration with a siamese spatiotemporal U-Net for joint arterial segmentation and change detection. Experiments on data from the MR CLEAN registry demonstrate that the proposed registration method significantly improves accuracy compared with the best-performing baseline (p=0.0054) and that the change-detection network consistently identifies arterial changes in successfully co-registered DSA pairs (Dice = 0.70). A preliminary reader study indicated that marking these changes can improve inter-rater agreement of extended thrombolysis in cerebral infarction (eTICI) scoring and increase the detection rate of emboli in new territory (ENT).