{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YJ4TRD4ORMN5XDRKBMHQAX4TP7","short_pith_number":"pith:YJ4TRD4O","schema_version":"1.0","canonical_sha256":"c279388f8e8b1bdb8e2a0b0f005f937fdd6d57a968429203309dc4ed702ea5e3","source":{"kind":"arxiv","id":"2507.07730","version":1},"attestation_state":"computed","paper":{"title":"RAPS-3D: Efficient interactive segmentation for 3D radiological imaging","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Corentin Dancette, Daniel Tordjman, Pierre Manceron, Th\\'eo Danielou","submitted_at":"2025-07-10T13:08:57Z","abstract_excerpt":"Promptable segmentation, introduced by the Segment Anything Model (SAM), is a promising approach for medical imaging, as it enables clinicians to guide and refine model predictions interactively. However, SAM's architecture is designed for 2D images and does not extend naturally to 3D volumetric data such as CT or MRI scans. Adapting 2D models to 3D typically involves autoregressive strategies, where predictions are propagated slice by slice, resulting in increased inference complexity. Processing large 3D volumes also requires significant computational resources, often leading existing 3D met"},"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":"2507.07730","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-10T13:08:57Z","cross_cats_sorted":[],"title_canon_sha256":"34b9a5724d9164a38873c223dc51f5ed3b41b0b74d3d0e9ae2a7001a2364e665","abstract_canon_sha256":"641d89448d5b38b337ef4869a8f202e352ce2894731c77b5861ac8e985795715"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:35:04.135075Z","signature_b64":"h26dWURgjk6PcF1PXW4vuuIAZBkBrq47ejm67r4+sPVXHO+ua9lvRL4vF+Y2zS9Cv5SDN2Hpp5xbyGFNgLdyCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c279388f8e8b1bdb8e2a0b0f005f937fdd6d57a968429203309dc4ed702ea5e3","last_reissued_at":"2026-07-05T11:35:04.134607Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:35:04.134607Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RAPS-3D: Efficient interactive segmentation for 3D radiological imaging","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Corentin Dancette, Daniel Tordjman, Pierre Manceron, Th\\'eo Danielou","submitted_at":"2025-07-10T13:08:57Z","abstract_excerpt":"Promptable segmentation, introduced by the Segment Anything Model (SAM), is a promising approach for medical imaging, as it enables clinicians to guide and refine model predictions interactively. However, SAM's architecture is designed for 2D images and does not extend naturally to 3D volumetric data such as CT or MRI scans. Adapting 2D models to 3D typically involves autoregressive strategies, where predictions are propagated slice by slice, resulting in increased inference complexity. Processing large 3D volumes also requires significant computational resources, often leading existing 3D met"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.07730","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/2507.07730/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":"2507.07730","created_at":"2026-07-05T11:35:04.134665+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.07730v1","created_at":"2026-07-05T11:35:04.134665+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.07730","created_at":"2026-07-05T11:35:04.134665+00:00"},{"alias_kind":"pith_short_12","alias_value":"YJ4TRD4ORMN5","created_at":"2026-07-05T11:35:04.134665+00:00"},{"alias_kind":"pith_short_16","alias_value":"YJ4TRD4ORMN5XDRK","created_at":"2026-07-05T11:35:04.134665+00:00"},{"alias_kind":"pith_short_8","alias_value":"YJ4TRD4O","created_at":"2026-07-05T11:35:04.134665+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/YJ4TRD4ORMN5XDRKBMHQAX4TP7","json":"https://pith.science/pith/YJ4TRD4ORMN5XDRKBMHQAX4TP7.json","graph_json":"https://pith.science/api/pith-number/YJ4TRD4ORMN5XDRKBMHQAX4TP7/graph.json","events_json":"https://pith.science/api/pith-number/YJ4TRD4ORMN5XDRKBMHQAX4TP7/events.json","paper":"https://pith.science/paper/YJ4TRD4O"},"agent_actions":{"view_html":"https://pith.science/pith/YJ4TRD4ORMN5XDRKBMHQAX4TP7","download_json":"https://pith.science/pith/YJ4TRD4ORMN5XDRKBMHQAX4TP7.json","view_paper":"https://pith.science/paper/YJ4TRD4O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.07730&json=true","fetch_graph":"https://pith.science/api/pith-number/YJ4TRD4ORMN5XDRKBMHQAX4TP7/graph.json","fetch_events":"https://pith.science/api/pith-number/YJ4TRD4ORMN5XDRKBMHQAX4TP7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YJ4TRD4ORMN5XDRKBMHQAX4TP7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YJ4TRD4ORMN5XDRKBMHQAX4TP7/action/storage_attestation","attest_author":"https://pith.science/pith/YJ4TRD4ORMN5XDRKBMHQAX4TP7/action/author_attestation","sign_citation":"https://pith.science/pith/YJ4TRD4ORMN5XDRKBMHQAX4TP7/action/citation_signature","submit_replication":"https://pith.science/pith/YJ4TRD4ORMN5XDRKBMHQAX4TP7/action/replication_record"}},"created_at":"2026-07-05T11:35:04.134665+00:00","updated_at":"2026-07-05T11:35:04.134665+00:00"}