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We developed a deep learning approach to automatically segment cerebrovascular-anatomical maps from multiple high-resolution magnetic resonance imaging\/angiography (MRI\/MRA) sequences in AVM patients, with the goal of facilitating target delineation. Twenty-three AVM patients who were evaluated for radiosurgery and underwent multi-parametric MRI\/MRA were included. A hybrid semi-automated and manual approach was used to label MRI\/MRAs with arteries, veins, brain parenchyma, cerebral spinal fluid (CSF), and embolized vessels. Next, these labels were used to train a convolutional neural network to perform this task. Imaging from 17 patients (6362 image slices) was used for training, and 6 patients (1224 slices) for validation. Performance was evaluated by Dice Similarity Coefficient (DSC). Classification performance was good for arteries, veins, brain parenchyma, and CSF, with DSCs of 0.86, 0.91, 0.98, and 0.91, respectively in the validation image set. Performance was lower for embolized vessels, with a DSC of 0.75. This demonstrates the proof of principle that accurate, high-resolution cerebrovascular-anatomical maps can be generated from multiparametric MRI\/MRA. Clinical validation of their utility in radiosurgery planning is warranted.<\/jats:p>","DOI":"10.1038\/s41598-021-04466-3","type":"journal-article","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T11:03:22Z","timestamp":1642417402000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Automated segmentation of multiparametric magnetic resonance images for cerebral AVM radiosurgery planning: a deep learning approach"],"prefix":"10.1038","volume":"12","author":[{"given":"Aaron B.","family":"Simon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Hurt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roshan","family":"Karunamuni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gwe-Ya","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vitali","family":"Moiseenko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott","family":"Olson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikdokht","family":"Farid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Albert","family":"Hsiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jona A.","family":"Hattangadi-Gluth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,17]]},"reference":[{"key":"4466_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0140-6736(02)07946-1","volume":"359","author":"I Fleetwood","year":"2002","unstructured":"Fleetwood, I. & Steinberg, G. 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Dr. Kim reports personal fees from Varian Medical Systems, outside the submitted work. Dr. Simon reports personal fees from Courage Health, Inc., outside the submitted work; In addition, Dr. Simon has a patent \"Novel method for automatic segmentation of vascular structures from multimodal MRI\/MRA\" pending. Dr. Hsiao reports research grants from and is a speaker for GE Healthcare, grants from Bayer AG, and is a co-founder and shareholder of Arterys, Inc., outside the submitted work; In addition, Dr. Hsiao has a patent Comprehensive Cardiovascular Analysis with Volumetric Phase-Contrast MRI with royalties paid to Stanford University. Dr. Hurt receives consulting fees from Imidex, outside of the submitted work. Dr. Karunamuni, Dr. Farid, Dr. Moiseenko, and Dr. Olson have no conflicts to report.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"786"}}