6851ddc383996b764fdbceb4835a5338 Automated_segmentation.pdf 701da8b1b9cd038642385e5788beee223c550e38 Automated_segmentation.pdf 4dfa7fbfd7930808e1268ddc4427105872a4d17fc530c52fdffa26f37e8edd85 Automated_segmentation.pdf Title: Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net Subject: Journal of Cardiovascular Magnetic Resonance, 27 (2025) 101923. doi:10.1016/j.jocmr.2025.101923 Keywords: Aorta; Aortic disease; Magnetic resonance angiography; Segmentation; Deep-learning; nnUNet; Author: Matteo Cesario Creator: Elsevier CreationDate: Mon Dec 15 16:40:15 2025 CET ModDate: Mon Dec 15 16:40:57 2025 CET Custom Metadata: yes Metadata Stream: yes Tagged: yes UserProperties: no Suspects: no Form: AcroForm JavaScript: no Pages: 14 Encrypted: no Page size: 595.276 x 793.701 pts Page rot: 0 File size: 9155954 bytes Optimized: yes PDF version: 1.7 name type encoding emb sub uni object ID 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Title: 2.4 Model(s) training Destination: mk:H2_8 Children: Item: Title: 2.4.1 Server specifics Destination: mk:H2_9 Item: Title: 2.4.2 Manual segmentation labels Destination: mk:H2_10 Item: Title: 2.4.3 The “nnU-Net” protocol Destination: mk:H2_11 Item: Title: 2.5 Inference and generalizability Destination: mk:H2_12 Item: Title: 2.6 Postprocessing: extracting clinically relevant information Destination: mk:H2_13 Item: Title: 2.7 Qualitative analysis and statistics Destination: mk:H2_14 Item: Title: 3 Results Destination: mk:H1_15 Children: Item: Title: 3.1 BRnnUNet model: aortic lumen segmentation on bright-blood 3D volumes Destination: mk:H2_16 Children: Item: Title: 3.1.1 Qualitative assessment of ground truth Destination: mk:H2_17 Item: Title: 3.1.2 U-Net model configuration(s) Destination: mk:H2_18 Item: Title: 3.1.3 Inference on test datasets Destination: mk:H2_19 Item: Title: 3.1.4 Visual representation of predicted segmentations Destination: mk:H2_20 Item: Title: 3.1.5 Qualitative assessment of predicted segmentations Destination: mk:H2_21 Item: Title: 3.1.6 Computational efficiency Destination: mk:H2_22 Item: Title: 3.2 BLnnUNet model: aortic lumen and wall segmentation on black-blood 3D volumes Destination: mk:H2_23 Children: Item: Title: 3.2.1 Qualitative assessment of ground truth Destination: mk:H2_24 Item: Title: 3.2.2 Inference on test datasets Destination: mk:H2_25 Item: Title: 3.2.3 Visual representation of predicted segmentations Destination: mk:H2_26 Item: Title: 3.2.4 Qualitative assessment of predicted segmentations Destination: mk:H2_27 Item: Title: 3.2.5 Computational efficiency Destination: mk:H2_28 Item: Title: 3.3 Postprocessing: extracting clinically relevant information Destination: mk:H2_29 Children: Item: Title: 3.3.1 Centreline extraction and (max) diameter measurement Destination: mk:H2_30 Item: Title: 3.3.2 Curved planar reformat Destination: mk:H2_31 Item: Title: 4 Discussion Destination: mk:H1_32 Item: Title: 5 Limitations Destination: mk:H1_33 Item: Title: 6 Conclusions Destination: mk:H1_34 Item: Title: Funding Destination: mk:H1_35 Item: Title: Author contributions Destination: mk:H1_36 Item: Title: mk:H1_37 Destination: mk:H1_37 Item: Title: Declaration of competing interests Destination: mk:H1_38 Item: Title: Appendix A Supporting information Destination: mk:H1_39 Item: Title: References Destination: mk:H1_40 Info: Title: Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net Author: Matteo Cesario Subject: Journal of Cardiovascular Magnetic Resonance, 27 (2025) 101923. doi:10.1016/j.jocmr.2025.101923 Keywords: Aorta; Aortic disease; Magnetic resonance angiography; Segmentation; Deep-learning; nnUNet; Creator: Elsevier CreationDate: Mon Dec 15 17:10:15 CET 2025 ModDate: Mon Dec 15 17:10:57 CET 2025 ID: 0x206ff6b3e37813ae4cc55ad7d576bc67, 0xa4d23c339bdcfa4db4cb5ebe0bedbf46 Filters: FilterPipeline: 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Supplement: 0 FontDescriptor: FontName: LYBKWD+TimesNewRoman Flags: Nonsymbolic FontBBox: -77, -225, 1009, 878 FontFile2: true XMP: application/pdf doi:10.1016/j.jocmr.2025.101923 Elsevier Journal of Cardiovascular Magnetic Resonance, 27 (2025) 101923. doi:10.1016/j.jocmr.2025.101923 Aorta Aortic disease Magnetic resonance angiography Segmentation Deep-learning nnUNet Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net Matteo Cesario Simon J. Littlewood James Nadel Thomas J. Fletcher Anastasia Fotaki Carlos Castillo-Passi Reza Hajhosseiny Jim Pouliopoulos Andrew Jabbour Ruperto Olivero Jose Rodríguez-Palomares M. Eline Kooi Claudia Prieto René M. Botnar journal Journal of Cardiovascular Magnetic Resonance © 2025 The Author(s). Published by Elsevier Inc. on behalf of Society for Cardiovascular Magnetic Resonance. 1097-6647 27 2 101923 101923 10.1016/j.jocmr.2025.101923 https://doi.org/10.1016/j.jocmr.2025.101923 2010-04-23 true 10.1016/j.jocmr.2025.101923 elsevier.com sciencedirect.com 7.0 10.1016/j.jocmr.2025.101923 noindex 2010-04-23 true elsevier.com sciencedirect.com VoR Elsevier 2025-12-15T21:10:57+05:30 2025-12-15T21:10:15+05:30 2025-12-15T21:10:57+05:30 True Aorta; Aortic disease; Magnetic resonance angiography; Segmentation; Deep-learning; nnUNet; 1 https://www.elsevier.com/tdm/tdmrep-policy.json uuid:e93dbf43-42f8-4c88-a2ec-858774747cd1 uuid:c8824b0c-06e7-4a20-b60a-65847798bebf https://creativecommons.org/licenses/BY/4.0/ Pages: Page: Sequence: 1 Annotations: Annotation: Subtype: Link Rect: 192, 750, 403, 757 Flags: Print Annotation: Subtype: Link Rect: 322, 723, 369, 730 Flags: Print Annotation: Subtype: Link Rect: 261, 667, 417, 675 Flags: Print Annotation: Subtype: Link Rect: 108, 562, 112, 573 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