{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:LWKIVEZ6DCOHSUJRIDZG2YJO6T","short_pith_number":"pith:LWKIVEZ6","schema_version":"1.0","canonical_sha256":"5d948a933e189c79513140f26d612ef4d44a1166e6cf39db4fcf1184f7bd5c77","source":{"kind":"arxiv","id":"2506.08280","version":1},"attestation_state":"computed","paper":{"title":"Snap-and-tune: combining deep learning and test-time optimization for high-fidelity cardiovascular volumetric meshing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Daniel H. Pak, Danny Bluestein, James S. Duncan, Kyle Baylous, Shubh Thaker, Xiaoran Zhang","submitted_at":"2025-06-09T22:58:11Z","abstract_excerpt":"High-quality volumetric meshing from medical images is a key bottleneck for physics-based simulations in personalized medicine. For volumetric meshing of complex medical structures, recent studies have often utilized deep learning (DL)-based template deformation approaches to enable fast test-time generation with high spatial accuracy. However, these approaches still exhibit limitations, such as limited flexibility at high-curvature areas and unrealistic inter-part distances. In this study, we introduce a simple yet effective snap-and-tune strategy that sequentially applies DL and test-time op"},"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":"2506.08280","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-06-09T22:58:11Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"76406cc853071a77e2b08d9dec94df306108eb56be1c3bdf2b4cbdd5b7671269","abstract_canon_sha256":"f749428ac8b98f04b321563983f95086a3a7244f4a366e4f42bde0c3a7a4153d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:47.327614Z","signature_b64":"qCFOK6JoRCH93AnCQ0PBhXSVjfouzdRPmjZq53UUx/FAEMK9LiBsERlqSza+Q74aI/2VXfKQl9aUdgLirG+LAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d948a933e189c79513140f26d612ef4d44a1166e6cf39db4fcf1184f7bd5c77","last_reissued_at":"2026-07-05T11:18:47.326941Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:47.326941Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Snap-and-tune: combining deep learning and test-time optimization for high-fidelity cardiovascular volumetric meshing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Daniel H. Pak, Danny Bluestein, James S. Duncan, Kyle Baylous, Shubh Thaker, Xiaoran Zhang","submitted_at":"2025-06-09T22:58:11Z","abstract_excerpt":"High-quality volumetric meshing from medical images is a key bottleneck for physics-based simulations in personalized medicine. For volumetric meshing of complex medical structures, recent studies have often utilized deep learning (DL)-based template deformation approaches to enable fast test-time generation with high spatial accuracy. However, these approaches still exhibit limitations, such as limited flexibility at high-curvature areas and unrealistic inter-part distances. In this study, we introduce a simple yet effective snap-and-tune strategy that sequentially applies DL and test-time op"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.08280","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/2506.08280/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":"2506.08280","created_at":"2026-07-05T11:18:47.327042+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.08280v1","created_at":"2026-07-05T11:18:47.327042+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.08280","created_at":"2026-07-05T11:18:47.327042+00:00"},{"alias_kind":"pith_short_12","alias_value":"LWKIVEZ6DCOH","created_at":"2026-07-05T11:18:47.327042+00:00"},{"alias_kind":"pith_short_16","alias_value":"LWKIVEZ6DCOHSUJR","created_at":"2026-07-05T11:18:47.327042+00:00"},{"alias_kind":"pith_short_8","alias_value":"LWKIVEZ6","created_at":"2026-07-05T11:18:47.327042+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/LWKIVEZ6DCOHSUJRIDZG2YJO6T","json":"https://pith.science/pith/LWKIVEZ6DCOHSUJRIDZG2YJO6T.json","graph_json":"https://pith.science/api/pith-number/LWKIVEZ6DCOHSUJRIDZG2YJO6T/graph.json","events_json":"https://pith.science/api/pith-number/LWKIVEZ6DCOHSUJRIDZG2YJO6T/events.json","paper":"https://pith.science/paper/LWKIVEZ6"},"agent_actions":{"view_html":"https://pith.science/pith/LWKIVEZ6DCOHSUJRIDZG2YJO6T","download_json":"https://pith.science/pith/LWKIVEZ6DCOHSUJRIDZG2YJO6T.json","view_paper":"https://pith.science/paper/LWKIVEZ6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.08280&json=true","fetch_graph":"https://pith.science/api/pith-number/LWKIVEZ6DCOHSUJRIDZG2YJO6T/graph.json","fetch_events":"https://pith.science/api/pith-number/LWKIVEZ6DCOHSUJRIDZG2YJO6T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LWKIVEZ6DCOHSUJRIDZG2YJO6T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LWKIVEZ6DCOHSUJRIDZG2YJO6T/action/storage_attestation","attest_author":"https://pith.science/pith/LWKIVEZ6DCOHSUJRIDZG2YJO6T/action/author_attestation","sign_citation":"https://pith.science/pith/LWKIVEZ6DCOHSUJRIDZG2YJO6T/action/citation_signature","submit_replication":"https://pith.science/pith/LWKIVEZ6DCOHSUJRIDZG2YJO6T/action/replication_record"}},"created_at":"2026-07-05T11:18:47.327042+00:00","updated_at":"2026-07-05T11:18:47.327042+00:00"}