{"paper":{"title":"MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Alessa Hering, Avan Kader, Bram van Ginneken, Christian J. Mertens, Christopher L. Schlett, Daniel Rueckert, Daniel Truhn, Fabian Bamberg, Felix Busch, Felix J. Dorfner, Hans-Ulrich Kauczor, Hartmut H\\\"antze, Henry V\\\"olzke, Hugo JWL Aerts, Jakob Wei{\\ss}, Jeanette Schulz-Menger, Julia Schnabel, Keno K. Bressem, Klaus Maier-Hein, Leonhard Donle, Lina Xu, Lisa C. Adams, Marcus R. Makowski, Mathias Prokop, Nadine Bayerl, Nassir Navab, Sebastian Ziegelmayer, Steffen Ringhof, Thomas Kr\\\"oncke, Thoralf Niendorf, Tobias Nonnenmacher, Tobias Pischon","submitted_at":"2024-05-10T13:15:42Z","abstract_excerpt":"Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans.\n  Materials and Methods: The model was trained on 1,200 manually annotated 3D axial MRI scans from the UK Biobank, 221 in-house MRI scans, and 1228 CT scans from the TotalSegmentator dataset. A human-in-the-loop annotation workflow was employed, leveraging cross-modality transfer learning from an existing CT segmentation model to segment 40 anatomical structures. The annotation process began with a model based on transfer learning between CT and MR, which was iteratively refined based on manual co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.06463","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2405.06463/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}