{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EKX33DJDMDERUNVX7BXNMWXET5","short_pith_number":"pith:EKX33DJD","schema_version":"1.0","canonical_sha256":"22afbd8d2360c91a36b7f86ed65ae49f7d00e9e780c08660c7c8135dfb51ac80","source":{"kind":"arxiv","id":"2405.06463","version":3},"attestation_state":"computed","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"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2405.06463","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-05-10T13:15:42Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"45b8d18edbd42c0eb5df1e5e57782130eb9fdef0177e6f6830394ee0fa747bb0","abstract_canon_sha256":"4640d2fe5d805e3a4e40f5f38b2798821e43340e86e51fb0403ede7b39783a74"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:50:20.311831Z","signature_b64":"mx7BgH15r/0nHKGiqDp+0xTXLt6a7Sn4qHLD3bCzZm38aAH8JypH83dnBaOFrWNjohDttNazl+AdLr9lViRMCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22afbd8d2360c91a36b7f86ed65ae49f7d00e9e780c08660c7c8135dfb51ac80","last_reissued_at":"2026-07-05T11:50:20.311347Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:50:20.311347Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2405.06463","created_at":"2026-07-05T11:50:20.311405+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.06463v3","created_at":"2026-07-05T11:50:20.311405+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.06463","created_at":"2026-07-05T11:50:20.311405+00:00"},{"alias_kind":"pith_short_12","alias_value":"EKX33DJDMDER","created_at":"2026-07-05T11:50:20.311405+00:00"},{"alias_kind":"pith_short_16","alias_value":"EKX33DJDMDERUNVX","created_at":"2026-07-05T11:50:20.311405+00:00"},{"alias_kind":"pith_short_8","alias_value":"EKX33DJD","created_at":"2026-07-05T11:50:20.311405+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.30108","citing_title":"LETT-NeXt: A Lightweight RECIST-Guided Model for 3D CT Lesion Segmentation","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EKX33DJDMDERUNVX7BXNMWXET5","json":"https://pith.science/pith/EKX33DJDMDERUNVX7BXNMWXET5.json","graph_json":"https://pith.science/api/pith-number/EKX33DJDMDERUNVX7BXNMWXET5/graph.json","events_json":"https://pith.science/api/pith-number/EKX33DJDMDERUNVX7BXNMWXET5/events.json","paper":"https://pith.science/paper/EKX33DJD"},"agent_actions":{"view_html":"https://pith.science/pith/EKX33DJDMDERUNVX7BXNMWXET5","download_json":"https://pith.science/pith/EKX33DJDMDERUNVX7BXNMWXET5.json","view_paper":"https://pith.science/paper/EKX33DJD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.06463&json=true","fetch_graph":"https://pith.science/api/pith-number/EKX33DJDMDERUNVX7BXNMWXET5/graph.json","fetch_events":"https://pith.science/api/pith-number/EKX33DJDMDERUNVX7BXNMWXET5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EKX33DJDMDERUNVX7BXNMWXET5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EKX33DJDMDERUNVX7BXNMWXET5/action/storage_attestation","attest_author":"https://pith.science/pith/EKX33DJDMDERUNVX7BXNMWXET5/action/author_attestation","sign_citation":"https://pith.science/pith/EKX33DJDMDERUNVX7BXNMWXET5/action/citation_signature","submit_replication":"https://pith.science/pith/EKX33DJDMDERUNVX7BXNMWXET5/action/replication_record"}},"created_at":"2026-07-05T11:50:20.311405+00:00","updated_at":"2026-07-05T11:50:20.311405+00:00"}