Deep reinforcement learning for automatic anatomic CT landmark localization in Stanford Type B aortic dissection.
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| Title: | Deep reinforcement learning for automatic anatomic CT landmark localization in Stanford Type B aortic dissection. |
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| Authors: | Bäumler K; Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, United States., Codari M; Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, United States., Mastrodicasa D; Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, United States., Mistelbauer G; Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, United States., Willemink MJ; Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, United States., Walters S; Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, United States., Hinostroza V; Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, United States., Turner V; Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, United States., Chepelev L; Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, United States., Sriprachyakul A; Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, United States.; Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, 10400, Thailand., Madani MH; Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, United States., Ewane A; Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, United States., Chen EP; Division of Cardiovascular and Thoracic Surgery, Duke University Medical Center, Durham, NC, 27710, United States., Marsden AL; Departments of Pediatrics and Bioengineering, Stanford University, Stanford, CA, 94305, United States., Desjardins B; Department of Radiology, University of Montreal, QC, H3C 3J7, Canada., Fleischmann D; Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, United States. |
| Corporate Authors: | ROADMAP Group |
| Source: | Radiology advances [Radiol Adv] 2026 Jan 21; Vol. 3 (2), pp. umag006. Date of Electronic Publication: 2026 Jan 21 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Oxford University Press Country of Publication: England NLM ID: 9918840888906676 Publication Model: eCollection Cited Medium: Internet ISSN: 2976-9337 (Electronic) Linking ISSN: 29769337 NLM ISO Abbreviation: Radiol Adv Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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