{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:HLBBRFS66KANWWGRLKMHMGB3HK","short_pith_number":"pith:HLBBRFS6","schema_version":"1.0","canonical_sha256":"3ac218965ef280db58d15a9876183b3aa8ac1d9d530db6605e940395492c1d35","source":{"kind":"arxiv","id":"2104.01985","version":1},"attestation_state":"computed","paper":{"title":"Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Aldo Marzullo, Benoit Rosa, Elena De Momi, Jorge F. Lazo, Michel de Mathelin, Michele Catellani, Sara Moccia","submitted_at":"2021-04-05T16:24:32Z","abstract_excerpt":"Purpose: Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma (UTUC). During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on Convolutional Neural Networks (CNNs).\n  Methods: The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of"},"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":"2104.01985","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-04-05T16:24:32Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"87ae4da458bc52c1ce5ed1659a5d8ca825059b8927083bdb6a37754bb7b75865","abstract_canon_sha256":"315228f0064a02f81eaad3c8dc58a5f8116ce7e0540c8ad1e9d57d403db078cc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:29:06.189270Z","signature_b64":"hw/aok4p98Tmw+j/nU3cP0mUokuWuFEFOanN6QBgJ1uzyh74BiS7U4Re/nCYpEDZbMAYEP4MTtzfBWUIvUe8BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3ac218965ef280db58d15a9876183b3aa8ac1d9d530db6605e940395492c1d35","last_reissued_at":"2026-07-05T02:29:06.188844Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:29:06.188844Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Aldo Marzullo, Benoit Rosa, Elena De Momi, Jorge F. Lazo, Michel de Mathelin, Michele Catellani, Sara Moccia","submitted_at":"2021-04-05T16:24:32Z","abstract_excerpt":"Purpose: Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma (UTUC). During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on Convolutional Neural Networks (CNNs).\n  Methods: The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.01985","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/2104.01985/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":"2104.01985","created_at":"2026-07-05T02:29:06.188894+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.01985v1","created_at":"2026-07-05T02:29:06.188894+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.01985","created_at":"2026-07-05T02:29:06.188894+00:00"},{"alias_kind":"pith_short_12","alias_value":"HLBBRFS66KAN","created_at":"2026-07-05T02:29:06.188894+00:00"},{"alias_kind":"pith_short_16","alias_value":"HLBBRFS66KANWWGR","created_at":"2026-07-05T02:29:06.188894+00:00"},{"alias_kind":"pith_short_8","alias_value":"HLBBRFS6","created_at":"2026-07-05T02:29:06.188894+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/HLBBRFS66KANWWGRLKMHMGB3HK","json":"https://pith.science/pith/HLBBRFS66KANWWGRLKMHMGB3HK.json","graph_json":"https://pith.science/api/pith-number/HLBBRFS66KANWWGRLKMHMGB3HK/graph.json","events_json":"https://pith.science/api/pith-number/HLBBRFS66KANWWGRLKMHMGB3HK/events.json","paper":"https://pith.science/paper/HLBBRFS6"},"agent_actions":{"view_html":"https://pith.science/pith/HLBBRFS66KANWWGRLKMHMGB3HK","download_json":"https://pith.science/pith/HLBBRFS66KANWWGRLKMHMGB3HK.json","view_paper":"https://pith.science/paper/HLBBRFS6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.01985&json=true","fetch_graph":"https://pith.science/api/pith-number/HLBBRFS66KANWWGRLKMHMGB3HK/graph.json","fetch_events":"https://pith.science/api/pith-number/HLBBRFS66KANWWGRLKMHMGB3HK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HLBBRFS66KANWWGRLKMHMGB3HK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HLBBRFS66KANWWGRLKMHMGB3HK/action/storage_attestation","attest_author":"https://pith.science/pith/HLBBRFS66KANWWGRLKMHMGB3HK/action/author_attestation","sign_citation":"https://pith.science/pith/HLBBRFS66KANWWGRLKMHMGB3HK/action/citation_signature","submit_replication":"https://pith.science/pith/HLBBRFS66KANWWGRLKMHMGB3HK/action/replication_record"}},"created_at":"2026-07-05T02:29:06.188894+00:00","updated_at":"2026-07-05T02:29:06.188894+00:00"}