{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:I5MSCNM2Y6VB2F3NWA7DRSRRKZ","short_pith_number":"pith:I5MSCNM2","schema_version":"1.0","canonical_sha256":"475921359ac7aa1d176db03e38ca31565c0fa79ef310e983f40bcd1354e5fe73","source":{"kind":"arxiv","id":"2411.14017","version":3},"attestation_state":"computed","paper":{"title":"Automatic brain tumor segmentation in 2D intra-operative ultrasound images using magnetic resonance imaging tumor annotations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Ingerid Reinertsen, Mathilde Faanes, Ole Solheim, Ragnhild Holden Helland, S\\'ebastien Muller","submitted_at":"2024-11-21T11:01:03Z","abstract_excerpt":"Automatic segmentation of brain tumors in intra-operative ultrasound (iUS) images could facilitate localization of tumor tissue during resection surgery. The lack of large annotated datasets limits the current models performances. In this paper, we investigated the use of tumor annotations in magnetic resonance imaging (MRI) scans, which are more accessible than annotations in iUS images, for training of deep learning models for iUS brain tumor segmentation. We used 180 annotated MRI scans with corresponding unannotated iUS images, and 29 annotated iUS images. Image registration was performed "},"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":"2411.14017","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-11-21T11:01:03Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"fb776e35ea8cb5f15695615c7824a8527be1e0829b59d1e5715d7efe3c4d72df","abstract_canon_sha256":"6833840e505af82e95a02399f805e568ff37764aec6acffde4fcbb4670040852"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:54:09.508709Z","signature_b64":"uA1gw9Zbw9QdCOgx/fIXom/Fg6VUWQw6w2U9FJ5W5HfnNlYyiHid7X365xLc/GhJEr0/oPyiH6wqCOYjYZADBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"475921359ac7aa1d176db03e38ca31565c0fa79ef310e983f40bcd1354e5fe73","last_reissued_at":"2026-07-05T11:54:09.508160Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:54:09.508160Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatic brain tumor segmentation in 2D intra-operative ultrasound images using magnetic resonance imaging tumor annotations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Ingerid Reinertsen, Mathilde Faanes, Ole Solheim, Ragnhild Holden Helland, S\\'ebastien Muller","submitted_at":"2024-11-21T11:01:03Z","abstract_excerpt":"Automatic segmentation of brain tumors in intra-operative ultrasound (iUS) images could facilitate localization of tumor tissue during resection surgery. The lack of large annotated datasets limits the current models performances. In this paper, we investigated the use of tumor annotations in magnetic resonance imaging (MRI) scans, which are more accessible than annotations in iUS images, for training of deep learning models for iUS brain tumor segmentation. We used 180 annotated MRI scans with corresponding unannotated iUS images, and 29 annotated iUS images. Image registration was performed "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.14017","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/2411.14017/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":"2411.14017","created_at":"2026-07-05T11:54:09.508225+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.14017v3","created_at":"2026-07-05T11:54:09.508225+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.14017","created_at":"2026-07-05T11:54:09.508225+00:00"},{"alias_kind":"pith_short_12","alias_value":"I5MSCNM2Y6VB","created_at":"2026-07-05T11:54:09.508225+00:00"},{"alias_kind":"pith_short_16","alias_value":"I5MSCNM2Y6VB2F3N","created_at":"2026-07-05T11:54:09.508225+00:00"},{"alias_kind":"pith_short_8","alias_value":"I5MSCNM2","created_at":"2026-07-05T11:54:09.508225+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.15994","citing_title":"Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating Room","ref_index":30,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I5MSCNM2Y6VB2F3NWA7DRSRRKZ","json":"https://pith.science/pith/I5MSCNM2Y6VB2F3NWA7DRSRRKZ.json","graph_json":"https://pith.science/api/pith-number/I5MSCNM2Y6VB2F3NWA7DRSRRKZ/graph.json","events_json":"https://pith.science/api/pith-number/I5MSCNM2Y6VB2F3NWA7DRSRRKZ/events.json","paper":"https://pith.science/paper/I5MSCNM2"},"agent_actions":{"view_html":"https://pith.science/pith/I5MSCNM2Y6VB2F3NWA7DRSRRKZ","download_json":"https://pith.science/pith/I5MSCNM2Y6VB2F3NWA7DRSRRKZ.json","view_paper":"https://pith.science/paper/I5MSCNM2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.14017&json=true","fetch_graph":"https://pith.science/api/pith-number/I5MSCNM2Y6VB2F3NWA7DRSRRKZ/graph.json","fetch_events":"https://pith.science/api/pith-number/I5MSCNM2Y6VB2F3NWA7DRSRRKZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I5MSCNM2Y6VB2F3NWA7DRSRRKZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I5MSCNM2Y6VB2F3NWA7DRSRRKZ/action/storage_attestation","attest_author":"https://pith.science/pith/I5MSCNM2Y6VB2F3NWA7DRSRRKZ/action/author_attestation","sign_citation":"https://pith.science/pith/I5MSCNM2Y6VB2F3NWA7DRSRRKZ/action/citation_signature","submit_replication":"https://pith.science/pith/I5MSCNM2Y6VB2F3NWA7DRSRRKZ/action/replication_record"}},"created_at":"2026-07-05T11:54:09.508225+00:00","updated_at":"2026-07-05T11:54:09.508225+00:00"}