{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:67AD2FEFRICH7NLXK6D2OTLIZT","short_pith_number":"pith:67AD2FEF","schema_version":"1.0","canonical_sha256":"f7c03d14858a047fb5775787a74d68ccc1de71a18b46a247974389eba3810e5b","source":{"kind":"arxiv","id":"2210.08068","version":1},"attestation_state":"computed","paper":{"title":"Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Lei Xiang, Ludovic Sibille, Xinrui Zhan","submitted_at":"2022-10-14T19:25:56Z","abstract_excerpt":"Background: A crucial initial processing step for quantitative PET/CT analysis is the segmentation of tumor lesions enabling accurate feature ex-traction, tumor characterization, oncologic staging, and image-based therapy response assessment. Manual lesion segmentation is however associated with enormous effort and cost and is thus infeasible in clinical routine. Goal: The goal of this study was to report the performance of a deep neural network designed to automatically segment regions suspected of cancer in whole-body 18F-FDG PET/CT images in the context of the AutoPET challenge. Method: A c"},"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":"2210.08068","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-10-14T19:25:56Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"664378ae7c8bc610b2c744d3e3aa1b995795dae884da787efebe5e8284eb3f38","abstract_canon_sha256":"6a4d72b9ee96cb3e04ff5dd1b6dc738dc0182767eaaa0c66c05087735b5d6a1f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:07:06.576633Z","signature_b64":"uW1GudVLoTy6gEhuUTeZiRKhyoKjhrjHPLWIS8tROtOJJuxbC3ScSAuA4ak2uXSffdeveNQTE6qfDMHxmxcxBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f7c03d14858a047fb5775787a74d68ccc1de71a18b46a247974389eba3810e5b","last_reissued_at":"2026-07-05T05:07:06.576153Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:07:06.576153Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Lei Xiang, Ludovic Sibille, Xinrui Zhan","submitted_at":"2022-10-14T19:25:56Z","abstract_excerpt":"Background: A crucial initial processing step for quantitative PET/CT analysis is the segmentation of tumor lesions enabling accurate feature ex-traction, tumor characterization, oncologic staging, and image-based therapy response assessment. Manual lesion segmentation is however associated with enormous effort and cost and is thus infeasible in clinical routine. Goal: The goal of this study was to report the performance of a deep neural network designed to automatically segment regions suspected of cancer in whole-body 18F-FDG PET/CT images in the context of the AutoPET challenge. Method: A c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.08068","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/2210.08068/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":"2210.08068","created_at":"2026-07-05T05:07:06.576211+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.08068v1","created_at":"2026-07-05T05:07:06.576211+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.08068","created_at":"2026-07-05T05:07:06.576211+00:00"},{"alias_kind":"pith_short_12","alias_value":"67AD2FEFRICH","created_at":"2026-07-05T05:07:06.576211+00:00"},{"alias_kind":"pith_short_16","alias_value":"67AD2FEFRICH7NLX","created_at":"2026-07-05T05:07:06.576211+00:00"},{"alias_kind":"pith_short_8","alias_value":"67AD2FEF","created_at":"2026-07-05T05:07:06.576211+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/67AD2FEFRICH7NLXK6D2OTLIZT","json":"https://pith.science/pith/67AD2FEFRICH7NLXK6D2OTLIZT.json","graph_json":"https://pith.science/api/pith-number/67AD2FEFRICH7NLXK6D2OTLIZT/graph.json","events_json":"https://pith.science/api/pith-number/67AD2FEFRICH7NLXK6D2OTLIZT/events.json","paper":"https://pith.science/paper/67AD2FEF"},"agent_actions":{"view_html":"https://pith.science/pith/67AD2FEFRICH7NLXK6D2OTLIZT","download_json":"https://pith.science/pith/67AD2FEFRICH7NLXK6D2OTLIZT.json","view_paper":"https://pith.science/paper/67AD2FEF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.08068&json=true","fetch_graph":"https://pith.science/api/pith-number/67AD2FEFRICH7NLXK6D2OTLIZT/graph.json","fetch_events":"https://pith.science/api/pith-number/67AD2FEFRICH7NLXK6D2OTLIZT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/67AD2FEFRICH7NLXK6D2OTLIZT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/67AD2FEFRICH7NLXK6D2OTLIZT/action/storage_attestation","attest_author":"https://pith.science/pith/67AD2FEFRICH7NLXK6D2OTLIZT/action/author_attestation","sign_citation":"https://pith.science/pith/67AD2FEFRICH7NLXK6D2OTLIZT/action/citation_signature","submit_replication":"https://pith.science/pith/67AD2FEFRICH7NLXK6D2OTLIZT/action/replication_record"}},"created_at":"2026-07-05T05:07:06.576211+00:00","updated_at":"2026-07-05T05:07:06.576211+00:00"}