Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
564-06-014 ISMRM Abstract

Development and Validation of Automated Intracranial Vessel Segmentation Based on a Heterogeneous MR Angiography Dataset

Primary: Neuro - Blood Vessels
Secondary: Analysis Methods - Segmentation and Detection
564-06-014 · Segmentation for Neuro Applications · Wednesday, 13 May, 4:55 PM–5:50 PM · Digital Posters Row E
Keywords: Cerebrovascular disease Cross-scanner generalizability Longitudinal analysis Automated intracranial vessel segmentation Reversible cerebral vasoconstriction syndrome
Accepted
Pei-Hsuan Kuo 1, Wei-Chen Chen1, Shih-Pin Chen2,3,4,5,6, Chia-Hung Wu2,4,7, Jiing-Feng Lirng2,7, Shuu-Jiun Wang2,3,5, Chia-Feng Lu1
1Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
2School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
3Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
4Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
5Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
6Division of Translational Research, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
7Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
Presenting Author: Pei-Hsuan Kuo

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Hu X, De Silva TM, Chen J, Faraci FM. Cerebral Vascular Disease and Neurovascular Injury in Ischemic Stroke. Circ Res. 2017 Feb 3;120(3):449-471. doi: 10.1161/CIRCRESAHA.116.308427. PMID: 28154097. [doi] [pmid]
2. Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, Lindley RI, O'Brien JT, Barkhof F, Benavente OR, Black SE, Brayne C, Breteler M, Chabriat H, Decarli C, de Leeuw FE, Doubal F, Duering M, Fox NC, Greenberg S, Hachinski V, Kilimann I, Mok V, Oostenbrugge Rv, Pantoni L, Speck O, Stephan BC, Teipel S, Viswanathan A, Werring D, Chen C, Smith C, van Buchem M, Norrving B, Gorelick PB, Dichgans M; STandards for ReportIng Vascular changes on nEuroimaging (STRIVE v1). Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013 Aug;12(8):822-38. doi: 10.1016/S1474-4422(13)70124-8. PMID: 23867200. [doi] [pmid]
3. Hilbert A, Rieger J, Madai VI, Akay EM, Aydin OU, Behland J, Khalil AA, Galinovic I, Sobesky J, Fiebach J, Livne M, Frey D. Anatomical labeling of intracranial arteries with deep learning in patients with cerebrovascular disease. Front Neurol. 2022 Oct 17;13:1000914. doi: 10.3389/fneur.2022.1000914. PMID: 36341105. [doi] [pmid]
4. Hilbert A, Madai VI, Akay EM, Aydin OU, Behland J, Sobesky J, Galinovic I, Khalil AA, Taha AA, Wuerfel J, Dusek P, Niendorf T, Fiebach JB, Frey D, Livne M. BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease. Front Artif Intell. 2020 Sep 25;3:552258. doi: 10.3389/frai.2020.552258. PMID: 33733207. [doi] [pmid]
5. Tetteh G, Efremov V, Forkert ND, Schneider M, Kirschke J, Weber B, Zimmer C, Piraud M, Menze BH. DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes. Front Neurosci. 2020 Dec 8;14:592352. doi: 10.3389/fnins.2020.592352. PMID: 33363452. [doi] [pmid]
6. Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. MICCAI 2016. Lecture Notes in Computer Science(), vol 9901. Springer, Cham. https://doi.org/10.1007/978-3-319-46723-8_49. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/564-06-014