{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:GUI472TJJ3U5NLFQ6RXBPXSC56","short_pith_number":"pith:GUI472TJ","schema_version":"1.0","canonical_sha256":"3511cfea694ee9d6acb0f46e17de42ef956222739a8da6d0a75df3435cc7996a","source":{"kind":"arxiv","id":"2208.09216","version":1},"attestation_state":"computed","paper":{"title":"Ensemble uncertainty as a criterion for dataset expansion in distinct bone segmentation from upper-body CT images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Antal Huck, Azhar Zam, Eva Schnider, Georg Rauter, Magdalena M\\\"uller-Gerbl, Mireille Toranelli, Philippe Cattin","submitted_at":"2022-08-19T08:39:23Z","abstract_excerpt":"Purpose: The localisation and segmentation of individual bones is an important preprocessing step in many planning and navigation applications. It is, however, a time-consuming and repetitive task if done manually. This is true not only for clinical practice but also for the acquisition of training data. We therefore not only present an end-to-end learnt algorithm that is capable of segmenting 125 distinct bones in an upper-body CT, but also provide an ensemble-based uncertainty measure that helps to single out scans to enlarge the training dataset with. Methods We create fully automated end-t"},"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":"2208.09216","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-08-19T08:39:23Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"97217b8bbd4f833b108c90a95298e70f10129fc81fdd0b5295c265c3dff0ae5e","abstract_canon_sha256":"cc5771e0dde1408a8045fe4cb8ece630d484617afa64f43e5e36abea747d904b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:49:57.376050Z","signature_b64":"LRSADxB25Bq8ncSXjtiz3DLxNCV00k/MYWPI1tJbQxk6AGmwa3cvxVGWW730fUBhMam1Zn8HDQDuqsgon/AcCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3511cfea694ee9d6acb0f46e17de42ef956222739a8da6d0a75df3435cc7996a","last_reissued_at":"2026-07-05T04:49:57.375543Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:49:57.375543Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ensemble uncertainty as a criterion for dataset expansion in distinct bone segmentation from upper-body CT images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Antal Huck, Azhar Zam, Eva Schnider, Georg Rauter, Magdalena M\\\"uller-Gerbl, Mireille Toranelli, Philippe Cattin","submitted_at":"2022-08-19T08:39:23Z","abstract_excerpt":"Purpose: The localisation and segmentation of individual bones is an important preprocessing step in many planning and navigation applications. It is, however, a time-consuming and repetitive task if done manually. This is true not only for clinical practice but also for the acquisition of training data. We therefore not only present an end-to-end learnt algorithm that is capable of segmenting 125 distinct bones in an upper-body CT, but also provide an ensemble-based uncertainty measure that helps to single out scans to enlarge the training dataset with. Methods We create fully automated end-t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.09216","kind":"arxiv","version":1},"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/2208.09216/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":"2208.09216","created_at":"2026-07-05T04:49:57.375652+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.09216v1","created_at":"2026-07-05T04:49:57.375652+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.09216","created_at":"2026-07-05T04:49:57.375652+00:00"},{"alias_kind":"pith_short_12","alias_value":"GUI472TJJ3U5","created_at":"2026-07-05T04:49:57.375652+00:00"},{"alias_kind":"pith_short_16","alias_value":"GUI472TJJ3U5NLFQ","created_at":"2026-07-05T04:49:57.375652+00:00"},{"alias_kind":"pith_short_8","alias_value":"GUI472TJ","created_at":"2026-07-05T04:49:57.375652+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GUI472TJJ3U5NLFQ6RXBPXSC56","json":"https://pith.science/pith/GUI472TJJ3U5NLFQ6RXBPXSC56.json","graph_json":"https://pith.science/api/pith-number/GUI472TJJ3U5NLFQ6RXBPXSC56/graph.json","events_json":"https://pith.science/api/pith-number/GUI472TJJ3U5NLFQ6RXBPXSC56/events.json","paper":"https://pith.science/paper/GUI472TJ"},"agent_actions":{"view_html":"https://pith.science/pith/GUI472TJJ3U5NLFQ6RXBPXSC56","download_json":"https://pith.science/pith/GUI472TJJ3U5NLFQ6RXBPXSC56.json","view_paper":"https://pith.science/paper/GUI472TJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.09216&json=true","fetch_graph":"https://pith.science/api/pith-number/GUI472TJJ3U5NLFQ6RXBPXSC56/graph.json","fetch_events":"https://pith.science/api/pith-number/GUI472TJJ3U5NLFQ6RXBPXSC56/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GUI472TJJ3U5NLFQ6RXBPXSC56/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GUI472TJJ3U5NLFQ6RXBPXSC56/action/storage_attestation","attest_author":"https://pith.science/pith/GUI472TJJ3U5NLFQ6RXBPXSC56/action/author_attestation","sign_citation":"https://pith.science/pith/GUI472TJJ3U5NLFQ6RXBPXSC56/action/citation_signature","submit_replication":"https://pith.science/pith/GUI472TJJ3U5NLFQ6RXBPXSC56/action/replication_record"}},"created_at":"2026-07-05T04:49:57.375652+00:00","updated_at":"2026-07-05T04:49:57.375652+00:00"}