{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:KRJ3ODBUNYIWLWGK3V2SLAQS3W","short_pith_number":"pith:KRJ3ODBU","canonical_record":{"source":{"id":"2309.03493","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-09-07T06:05:28Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"7383e2d3afe2726bce75a28d6ca695573bfa1cef3a9ff2ac24841ca486e7c847","abstract_canon_sha256":"c35a3a7192bf697a564c08a2ce496f1b717eecc5a8017c706bfbc91757140449"},"schema_version":"1.0"},"canonical_sha256":"5453b70c346e1165d8cadd75258212ddb22654f88784b9f4523d42faa52e376e","source":{"kind":"arxiv","id":"2309.03493","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.03493","created_at":"2026-07-05T07:52:41Z"},{"alias_kind":"arxiv_version","alias_value":"2309.03493v4","created_at":"2026-07-05T07:52:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.03493","created_at":"2026-07-05T07:52:41Z"},{"alias_kind":"pith_short_12","alias_value":"KRJ3ODBUNYIW","created_at":"2026-07-05T07:52:41Z"},{"alias_kind":"pith_short_16","alias_value":"KRJ3ODBUNYIWLWGK","created_at":"2026-07-05T07:52:41Z"},{"alias_kind":"pith_short_8","alias_value":"KRJ3ODBU","created_at":"2026-07-05T07:52:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:KRJ3ODBUNYIWLWGK3V2SLAQS3W","target":"record","payload":{"canonical_record":{"source":{"id":"2309.03493","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-09-07T06:05:28Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"7383e2d3afe2726bce75a28d6ca695573bfa1cef3a9ff2ac24841ca486e7c847","abstract_canon_sha256":"c35a3a7192bf697a564c08a2ce496f1b717eecc5a8017c706bfbc91757140449"},"schema_version":"1.0"},"canonical_sha256":"5453b70c346e1165d8cadd75258212ddb22654f88784b9f4523d42faa52e376e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:52:41.636383Z","signature_b64":"oUIoCxOzuUU89T2g/jopU9WtVC+/pVup1SyxPajHNDQre/1LgoxIi8BADdt4ZPALbJhXDwEw+ybLscM3BU6PAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5453b70c346e1165d8cadd75258212ddb22654f88784b9f4523d42faa52e376e","last_reissued_at":"2026-07-05T07:52:41.635827Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:52:41.635827Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2309.03493","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:52:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P+RpLAOhjqUpuhR+XaSM9+pY1lYSJoR/E0dE8eBoeeNrumCBFjlhyhbfLiJaAONr7Ug57ptCEkZczG8iaZ4yCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T13:25:36.863051Z"},"content_sha256":"36f86594d38c64d470b6b9ecdd90b13576ea8778d2c46afb5f0cf73773734eb3","schema_version":"1.0","event_id":"sha256:36f86594d38c64d470b6b9ecdd90b13576ea8778d2c46afb5f0cf73773734eb3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:KRJ3ODBUNYIWLWGK3V2SLAQS3W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SAM3D: Segment Anything Model in Volumetric Medical Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Arabinda Choudhary, Brijesh Patel, Dinh-Hieu Hoang, Donald Adjeroh, Gianfranco Doretto, Minh-Triet Tran, Ngan Le, Nhat-Tan Bui","submitted_at":"2023-09-07T06:05:28Z","abstract_excerpt":"Image segmentation remains a pivotal component in medical image analysis, aiding in the extraction of critical information for precise diagnostic practices. With the advent of deep learning, automated image segmentation methods have risen to prominence, showcasing exceptional proficiency in processing medical imagery. Motivated by the Segment Anything Model (SAM)-a foundational model renowned for its remarkable precision and robust generalization capabilities in segmenting 2D natural images-we introduce SAM3D, an innovative adaptation tailored for 3D volumetric medical image analysis. Unlike c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.03493","kind":"arxiv","version":4},"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/2309.03493/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:52:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hy8zzDPKN7z1+OtFrGghYpfeU3a1+nyGjCHKNnB4IT8W8rD9xqG7xJGtOgwmIZ8GltyM8Jq1+nDn/pnLii0FBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T13:25:36.863831Z"},"content_sha256":"d2eb4d1aee3d2bf5e6f7890ff4b5c41615e33dd95adbf55ac38da1d78ee29d7a","schema_version":"1.0","event_id":"sha256:d2eb4d1aee3d2bf5e6f7890ff4b5c41615e33dd95adbf55ac38da1d78ee29d7a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KRJ3ODBUNYIWLWGK3V2SLAQS3W/bundle.json","state_url":"https://pith.science/pith/KRJ3ODBUNYIWLWGK3V2SLAQS3W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KRJ3ODBUNYIWLWGK3V2SLAQS3W/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-10T13:25:36Z","links":{"resolver":"https://pith.science/pith/KRJ3ODBUNYIWLWGK3V2SLAQS3W","bundle":"https://pith.science/pith/KRJ3ODBUNYIWLWGK3V2SLAQS3W/bundle.json","state":"https://pith.science/pith/KRJ3ODBUNYIWLWGK3V2SLAQS3W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KRJ3ODBUNYIWLWGK3V2SLAQS3W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:KRJ3ODBUNYIWLWGK3V2SLAQS3W","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c35a3a7192bf697a564c08a2ce496f1b717eecc5a8017c706bfbc91757140449","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-09-07T06:05:28Z","title_canon_sha256":"7383e2d3afe2726bce75a28d6ca695573bfa1cef3a9ff2ac24841ca486e7c847"},"schema_version":"1.0","source":{"id":"2309.03493","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.03493","created_at":"2026-07-05T07:52:41Z"},{"alias_kind":"arxiv_version","alias_value":"2309.03493v4","created_at":"2026-07-05T07:52:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.03493","created_at":"2026-07-05T07:52:41Z"},{"alias_kind":"pith_short_12","alias_value":"KRJ3ODBUNYIW","created_at":"2026-07-05T07:52:41Z"},{"alias_kind":"pith_short_16","alias_value":"KRJ3ODBUNYIWLWGK","created_at":"2026-07-05T07:52:41Z"},{"alias_kind":"pith_short_8","alias_value":"KRJ3ODBU","created_at":"2026-07-05T07:52:41Z"}],"graph_snapshots":[{"event_id":"sha256:d2eb4d1aee3d2bf5e6f7890ff4b5c41615e33dd95adbf55ac38da1d78ee29d7a","target":"graph","created_at":"2026-07-05T07:52:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2309.03493/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Image segmentation remains a pivotal component in medical image analysis, aiding in the extraction of critical information for precise diagnostic practices. With the advent of deep learning, automated image segmentation methods have risen to prominence, showcasing exceptional proficiency in processing medical imagery. Motivated by the Segment Anything Model (SAM)-a foundational model renowned for its remarkable precision and robust generalization capabilities in segmenting 2D natural images-we introduce SAM3D, an innovative adaptation tailored for 3D volumetric medical image analysis. Unlike c","authors_text":"Arabinda Choudhary, Brijesh Patel, Dinh-Hieu Hoang, Donald Adjeroh, Gianfranco Doretto, Minh-Triet Tran, Ngan Le, Nhat-Tan Bui","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-09-07T06:05:28Z","title":"SAM3D: Segment Anything Model in Volumetric Medical Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.03493","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:36f86594d38c64d470b6b9ecdd90b13576ea8778d2c46afb5f0cf73773734eb3","target":"record","created_at":"2026-07-05T07:52:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c35a3a7192bf697a564c08a2ce496f1b717eecc5a8017c706bfbc91757140449","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-09-07T06:05:28Z","title_canon_sha256":"7383e2d3afe2726bce75a28d6ca695573bfa1cef3a9ff2ac24841ca486e7c847"},"schema_version":"1.0","source":{"id":"2309.03493","kind":"arxiv","version":4}},"canonical_sha256":"5453b70c346e1165d8cadd75258212ddb22654f88784b9f4523d42faa52e376e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5453b70c346e1165d8cadd75258212ddb22654f88784b9f4523d42faa52e376e","first_computed_at":"2026-07-05T07:52:41.635827Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:52:41.635827Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oUIoCxOzuUU89T2g/jopU9WtVC+/pVup1SyxPajHNDQre/1LgoxIi8BADdt4ZPALbJhXDwEw+ybLscM3BU6PAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:52:41.636383Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.03493","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:36f86594d38c64d470b6b9ecdd90b13576ea8778d2c46afb5f0cf73773734eb3","sha256:d2eb4d1aee3d2bf5e6f7890ff4b5c41615e33dd95adbf55ac38da1d78ee29d7a"],"state_sha256":"c7e4d33d7a239995c6e5a2d2219df547f3f5e98e50f26e5a377099a010b0fb13"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"51VQfmkmIir/IOUAXLR9Oy/YUf7jbFWi89LgZMlx8c0JJw1s8EaZ0jLeOocXj2q1AMi9tN/gCbfns4MtcUWwCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T13:25:36.870165Z","bundle_sha256":"513477ea89b6a27324006771f4f556d894d049d7f0637e0ed9e452bbdbf512a3"}}