{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:H5UH3NIRYJW3WAPUWCW4WEKEO3","short_pith_number":"pith:H5UH3NIR","schema_version":"1.0","canonical_sha256":"3f687db511c26dbb01f4b0adcb114476f1e12cd33dbec1d6c4e20bb1bb75d586","source":{"kind":"arxiv","id":"2607.18014","version":1},"attestation_state":"computed","paper":{"title":"SAMRI-3D: Adapting SAM2 for 3D MRI Segmentation with Global Volume Tokens","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Craig Engstrom, Hongfu Sun, Shekhar S. Chandra, Wei Dai, Zhao Wang","submitted_at":"2026-07-20T14:48:11Z","abstract_excerpt":"Foundation models such as Segment Anything Model 2 (SAM2) have transformed natural-image and video segmentation, and recent work has begun adapting them to medical imaging. These adaptations, however, are largely general-purpose models that treat MRI as one modality among many; large-scale, MRI-specific modelling and benchmarking remain limited, even though MRI's low soft-tissue contrast leaves many boundaries effectively invisible on individual slices. We present SAMRI-3D, a benchmark and method for 3D MRI segmentation with SAM2. The SAMRI-3D benchmark is the largest MRI-only evaluation to da"},"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":"2607.18014","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-20T14:48:11Z","cross_cats_sorted":[],"title_canon_sha256":"46990620528b09be89d31b4dd8638b467fc91790307358aef401c61bd5b9e577","abstract_canon_sha256":"471e77ed9c0a8fc3482b2bc1a75ba00c974f1de149df2f00cdf96cf92c7da61e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T02:22:11.823787Z","signature_b64":"7m3l+S6DC63opWyMT3HVDEGbDO/9bj48f85OJrfdR8JmAhoQpTLuDXaCT+5QtPKCuSWWVo22bM0r0aH48yc7Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f687db511c26dbb01f4b0adcb114476f1e12cd33dbec1d6c4e20bb1bb75d586","last_reissued_at":"2026-07-21T02:22:11.823007Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T02:22:11.823007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SAMRI-3D: Adapting SAM2 for 3D MRI Segmentation with Global Volume Tokens","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Craig Engstrom, Hongfu Sun, Shekhar S. Chandra, Wei Dai, Zhao Wang","submitted_at":"2026-07-20T14:48:11Z","abstract_excerpt":"Foundation models such as Segment Anything Model 2 (SAM2) have transformed natural-image and video segmentation, and recent work has begun adapting them to medical imaging. These adaptations, however, are largely general-purpose models that treat MRI as one modality among many; large-scale, MRI-specific modelling and benchmarking remain limited, even though MRI's low soft-tissue contrast leaves many boundaries effectively invisible on individual slices. We present SAMRI-3D, a benchmark and method for 3D MRI segmentation with SAM2. The SAMRI-3D benchmark is the largest MRI-only evaluation to da"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.18014","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/2607.18014/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":"2607.18014","created_at":"2026-07-21T02:22:11.823428+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.18014v1","created_at":"2026-07-21T02:22:11.823428+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.18014","created_at":"2026-07-21T02:22:11.823428+00:00"},{"alias_kind":"pith_short_12","alias_value":"H5UH3NIRYJW3","created_at":"2026-07-21T02:22:11.823428+00:00"},{"alias_kind":"pith_short_16","alias_value":"H5UH3NIRYJW3WAPU","created_at":"2026-07-21T02:22:11.823428+00:00"},{"alias_kind":"pith_short_8","alias_value":"H5UH3NIR","created_at":"2026-07-21T02:22:11.823428+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/H5UH3NIRYJW3WAPUWCW4WEKEO3","json":"https://pith.science/pith/H5UH3NIRYJW3WAPUWCW4WEKEO3.json","graph_json":"https://pith.science/api/pith-number/H5UH3NIRYJW3WAPUWCW4WEKEO3/graph.json","events_json":"https://pith.science/api/pith-number/H5UH3NIRYJW3WAPUWCW4WEKEO3/events.json","paper":"https://pith.science/paper/H5UH3NIR"},"agent_actions":{"view_html":"https://pith.science/pith/H5UH3NIRYJW3WAPUWCW4WEKEO3","download_json":"https://pith.science/pith/H5UH3NIRYJW3WAPUWCW4WEKEO3.json","view_paper":"https://pith.science/paper/H5UH3NIR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.18014&json=true","fetch_graph":"https://pith.science/api/pith-number/H5UH3NIRYJW3WAPUWCW4WEKEO3/graph.json","fetch_events":"https://pith.science/api/pith-number/H5UH3NIRYJW3WAPUWCW4WEKEO3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H5UH3NIRYJW3WAPUWCW4WEKEO3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H5UH3NIRYJW3WAPUWCW4WEKEO3/action/storage_attestation","attest_author":"https://pith.science/pith/H5UH3NIRYJW3WAPUWCW4WEKEO3/action/author_attestation","sign_citation":"https://pith.science/pith/H5UH3NIRYJW3WAPUWCW4WEKEO3/action/citation_signature","submit_replication":"https://pith.science/pith/H5UH3NIRYJW3WAPUWCW4WEKEO3/action/replication_record"}},"created_at":"2026-07-21T02:22:11.823428+00:00","updated_at":"2026-07-21T02:22:11.823428+00:00"}