{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:YYUIGAWD5DUCOLO2OMF4JLBBCT","short_pith_number":"pith:YYUIGAWD","schema_version":"1.0","canonical_sha256":"c6288302c3e8e8272dda730bc4ac2114de83365a693a9db42a4f48310bbb6515","source":{"kind":"arxiv","id":"2604.10245","version":2},"attestation_state":"computed","paper":{"title":"Warm-Started Reinforcement Learning for Iterative 3D/2D Liver Registration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A warm-started RL policy performs iterative 6-DoF CT-to-video liver registration and learns its own stopping criterion, reaching 15.70 mm TRE without extra optimization.","cross_cats":["physics.med-ph"],"primary_cat":"cs.CV","authors_text":"Abdolrahim Kadkhodamohammadi, Brian R. Davidson, Danail Stoyanov, Evangelos B. Mazomenos, Hanyuan Zhang, Lucas He, Matthew.J Clarkson, Zijie Cheng","submitted_at":"2026-04-11T14:58:45Z","abstract_excerpt":"Registration between preoperative CT and intraoperative laparoscopic video plays a crucial role in augmented reality (AR) guidance for minimally invasive surgery. Learning-based methods have recently achieved registration errors comparable to optimization-based approaches while offering faster inference. However, many supervised methods produce coarse alignments that rely on additional optimization-based refinement, thereby increasing inference time.\n  We present a discrete-action reinforcement learning (RL) framework that formulates CT-to-video registration as a sequential decision-making pro"},"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":"2604.10245","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-11T14:58:45Z","cross_cats_sorted":["physics.med-ph"],"title_canon_sha256":"00f63f46f2c6f3dc2d0f9c3de92dd1faa4b4a7f651a1b1dd32af409a123169a7","abstract_canon_sha256":"ee9d40fb134b93f8d65eeb3b59cd6facb57342c0a5c15ca99a3538b1429656bd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T01:05:18.811565Z","signature_b64":"y8v8fu7ilNQag/cexqMeN4h6cBJ5+MNMgsKLI6P/pgxWBe9eugMLZG1Mz/Fa5ytybGnBKSC+nDLSPKpmhTsbAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6288302c3e8e8272dda730bc4ac2114de83365a693a9db42a4f48310bbb6515","last_reissued_at":"2026-05-21T01:05:18.811077Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T01:05:18.811077Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Warm-Started Reinforcement Learning for Iterative 3D/2D Liver Registration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A warm-started RL policy performs iterative 6-DoF CT-to-video liver registration and learns its own stopping criterion, reaching 15.70 mm TRE without extra optimization.","cross_cats":["physics.med-ph"],"primary_cat":"cs.CV","authors_text":"Abdolrahim Kadkhodamohammadi, Brian R. Davidson, Danail Stoyanov, Evangelos B. Mazomenos, Hanyuan Zhang, Lucas He, Matthew.J Clarkson, Zijie Cheng","submitted_at":"2026-04-11T14:58:45Z","abstract_excerpt":"Registration between preoperative CT and intraoperative laparoscopic video plays a crucial role in augmented reality (AR) guidance for minimally invasive surgery. Learning-based methods have recently achieved registration errors comparable to optimization-based approaches while offering faster inference. However, many supervised methods produce coarse alignments that rely on additional optimization-based refinement, thereby increasing inference time.\n  We present a discrete-action reinforcement learning (RL) framework that formulates CT-to-video registration as a sequential decision-making pro"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Experiments on a public laparoscopic dataset demonstrated that our method achieved an average target registration error (TRE) of 15.70 mm, comparable to supervised approaches with optimization, while achieving faster convergence.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The warm-started RL policy can reliably learn effective 6-DoF rigid transformations and a stopping criterion from the shared encoder features without post-hoc tuning or overfitting to the specific dataset.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A warm-started discrete-action RL framework for CT-to-video liver registration achieves 15.70 mm average TRE with faster convergence than supervised methods plus optimization.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A warm-started RL policy performs iterative 6-DoF CT-to-video liver registration and learns its own stopping criterion, reaching 15.70 mm TRE without extra optimization.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"771a98c4eccbca92b4dbc402172d845fef535e5af946b0c296ca0731b934d31e"},"source":{"id":"2604.10245","kind":"arxiv","version":2},"verdict":{"id":"e9b7ba05-1384-4ca4-9e96-69e158994c22","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T16:28:53.192594Z","strongest_claim":"Experiments on a public laparoscopic dataset demonstrated that our method achieved an average target registration error (TRE) of 15.70 mm, comparable to supervised approaches with optimization, while achieving faster convergence.","one_line_summary":"A warm-started discrete-action RL framework for CT-to-video liver registration achieves 15.70 mm average TRE with faster convergence than supervised methods plus optimization.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The warm-started RL policy can reliably learn effective 6-DoF rigid transformations and a stopping criterion from the shared encoder features without post-hoc tuning or overfitting to the specific dataset.","pith_extraction_headline":"A warm-started RL policy performs iterative 6-DoF CT-to-video liver registration and learns its own stopping criterion, reaching 15.70 mm TRE without extra optimization."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.10245/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":"2604.10245","created_at":"2026-05-21T01:05:18.811136+00:00"},{"alias_kind":"arxiv_version","alias_value":"2604.10245v2","created_at":"2026-05-21T01:05:18.811136+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.10245","created_at":"2026-05-21T01:05:18.811136+00:00"},{"alias_kind":"pith_short_12","alias_value":"YYUIGAWD5DUC","created_at":"2026-05-21T01:05:18.811136+00:00"},{"alias_kind":"pith_short_16","alias_value":"YYUIGAWD5DUCOLO2","created_at":"2026-05-21T01:05:18.811136+00:00"},{"alias_kind":"pith_short_8","alias_value":"YYUIGAWD","created_at":"2026-05-21T01:05:18.811136+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/YYUIGAWD5DUCOLO2OMF4JLBBCT","json":"https://pith.science/pith/YYUIGAWD5DUCOLO2OMF4JLBBCT.json","graph_json":"https://pith.science/api/pith-number/YYUIGAWD5DUCOLO2OMF4JLBBCT/graph.json","events_json":"https://pith.science/api/pith-number/YYUIGAWD5DUCOLO2OMF4JLBBCT/events.json","paper":"https://pith.science/paper/YYUIGAWD"},"agent_actions":{"view_html":"https://pith.science/pith/YYUIGAWD5DUCOLO2OMF4JLBBCT","download_json":"https://pith.science/pith/YYUIGAWD5DUCOLO2OMF4JLBBCT.json","view_paper":"https://pith.science/paper/YYUIGAWD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2604.10245&json=true","fetch_graph":"https://pith.science/api/pith-number/YYUIGAWD5DUCOLO2OMF4JLBBCT/graph.json","fetch_events":"https://pith.science/api/pith-number/YYUIGAWD5DUCOLO2OMF4JLBBCT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YYUIGAWD5DUCOLO2OMF4JLBBCT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YYUIGAWD5DUCOLO2OMF4JLBBCT/action/storage_attestation","attest_author":"https://pith.science/pith/YYUIGAWD5DUCOLO2OMF4JLBBCT/action/author_attestation","sign_citation":"https://pith.science/pith/YYUIGAWD5DUCOLO2OMF4JLBBCT/action/citation_signature","submit_replication":"https://pith.science/pith/YYUIGAWD5DUCOLO2OMF4JLBBCT/action/replication_record"}},"created_at":"2026-05-21T01:05:18.811136+00:00","updated_at":"2026-05-21T01:05:18.811136+00:00"}