{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:QHC3T3GO3RUXCW4A42EVROVBNS","short_pith_number":"pith:QHC3T3GO","schema_version":"1.0","canonical_sha256":"81c5b9eccedc69715b80e68958baa16ca879ea7f3e49359eafa05b08d7650e0e","source":{"kind":"arxiv","id":"1906.10288","version":2},"attestation_state":"computed","paper":{"title":"3DBGrowth: volumetric vertebrae segmentation and reconstruction in magnetic resonance imaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Agma J. M. Traina, Bruno S. Fai\\c{c}al, Caetano Traina Jr., Jonathan S. Ramos, Marcello H. Nogueira-Barbosa, Mirela T. Cazzolato","submitted_at":"2019-06-25T01:28:20Z","abstract_excerpt":"Segmentation of medical images is critical for making several processes of analysis and classification more reliable. With the growing number of people presenting back pain and related problems, the semi-automatic segmentation and 3D reconstruction of vertebral bodies became even more important to support decision making. A 3D reconstruction allows a fast and objective analysis of each vertebrae condition, which may play a major role in surgical planning and evaluation of suitable treatments. In this paper, we propose 3DBGrowth, which develops a 3D reconstruction over the efficient Balanced Gr"},"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":"1906.10288","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-25T01:28:20Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"b51d4c5122e78a4e254cc4c603f21a8cb1a73a82a1bf97635c01b86ca5fbb370","abstract_canon_sha256":"97a592b204eb600d38568f857e5c7d9ce66f801fec06f97fc85a579c693a20ae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:07.446020Z","signature_b64":"eTOq2XDykCxDQgkU3OYT+BF+c8IM2wDJywH53sBBnxOjwdLuzYlDT2CsEwQQ9pVeosFvxDYI0W/ljwHTJAU+Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81c5b9eccedc69715b80e68958baa16ca879ea7f3e49359eafa05b08d7650e0e","last_reissued_at":"2026-05-17T23:41:07.445470Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:07.445470Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"3DBGrowth: volumetric vertebrae segmentation and reconstruction in magnetic resonance imaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Agma J. M. Traina, Bruno S. Fai\\c{c}al, Caetano Traina Jr., Jonathan S. Ramos, Marcello H. Nogueira-Barbosa, Mirela T. Cazzolato","submitted_at":"2019-06-25T01:28:20Z","abstract_excerpt":"Segmentation of medical images is critical for making several processes of analysis and classification more reliable. With the growing number of people presenting back pain and related problems, the semi-automatic segmentation and 3D reconstruction of vertebral bodies became even more important to support decision making. A 3D reconstruction allows a fast and objective analysis of each vertebrae condition, which may play a major role in surgical planning and evaluation of suitable treatments. In this paper, we propose 3DBGrowth, which develops a 3D reconstruction over the efficient Balanced Gr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.10288","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1906.10288","created_at":"2026-05-17T23:41:07.445560+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.10288v2","created_at":"2026-05-17T23:41:07.445560+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.10288","created_at":"2026-05-17T23:41:07.445560+00:00"},{"alias_kind":"pith_short_12","alias_value":"QHC3T3GO3RUX","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"QHC3T3GO3RUXCW4A","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"QHC3T3GO","created_at":"2026-05-18T12:33:27.125529+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/QHC3T3GO3RUXCW4A42EVROVBNS","json":"https://pith.science/pith/QHC3T3GO3RUXCW4A42EVROVBNS.json","graph_json":"https://pith.science/api/pith-number/QHC3T3GO3RUXCW4A42EVROVBNS/graph.json","events_json":"https://pith.science/api/pith-number/QHC3T3GO3RUXCW4A42EVROVBNS/events.json","paper":"https://pith.science/paper/QHC3T3GO"},"agent_actions":{"view_html":"https://pith.science/pith/QHC3T3GO3RUXCW4A42EVROVBNS","download_json":"https://pith.science/pith/QHC3T3GO3RUXCW4A42EVROVBNS.json","view_paper":"https://pith.science/paper/QHC3T3GO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.10288&json=true","fetch_graph":"https://pith.science/api/pith-number/QHC3T3GO3RUXCW4A42EVROVBNS/graph.json","fetch_events":"https://pith.science/api/pith-number/QHC3T3GO3RUXCW4A42EVROVBNS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QHC3T3GO3RUXCW4A42EVROVBNS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QHC3T3GO3RUXCW4A42EVROVBNS/action/storage_attestation","attest_author":"https://pith.science/pith/QHC3T3GO3RUXCW4A42EVROVBNS/action/author_attestation","sign_citation":"https://pith.science/pith/QHC3T3GO3RUXCW4A42EVROVBNS/action/citation_signature","submit_replication":"https://pith.science/pith/QHC3T3GO3RUXCW4A42EVROVBNS/action/replication_record"}},"created_at":"2026-05-17T23:41:07.445560+00:00","updated_at":"2026-05-17T23:41:07.445560+00:00"}