Deep Learning-based Automated Vessel Extraction for VR Image Creation From Cerebral TOF-MRA.

Kota Kawahara¹, Shinpei Sato², Daisuke Oura³

¹Department of Radiology, Otaru General Hospital, 1-1-1 Wakamatsu, Otaru, Hokkaido, Japan (K.K., S.S., D.O.). Electronic address: kheyuan16@gmail.com.

²Department of Radiology, Otaru General Hospital, 1-1-1 Wakamatsu, Otaru, Hokkaido, Japan (K.K., S.S., D.O.). Electronic address: satoh.shin1102@gmail.com.

³Department of Radiology, Otaru General Hospital, 1-1-1 Wakamatsu, Otaru, Hokkaido, Japan (K.K., S.S., D.O.); Department of Biomedical Science and Engineering, Faculty of Health Sciences, Hokkaido University, Sapporo, Hokkaido, Japan (D.O.). Electronic address: daisu.k.oura@otaru-general-hospital.jp.

PubMed DOI

Abstract

RATIONALE AND OBJECTIVES: Accurate and efficient three-dimensional visualization of cerebral vasculature is essential for clinical evaluation; however, manual vessel extraction from time-of-flight (TOF) magnetic resonance angiography angiography (MRA) is time-consuming and operator-dependent. This study aimed to develop a deep learning-based cerebrovascular segmentation model and an automated vess…

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