{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:53PW4T6WLBTDQ6BXNMOU7PAJGJ","short_pith_number":"pith:53PW4T6W","schema_version":"1.0","canonical_sha256":"eedf6e4fd658663878376b1d4fbc09326b5861c491317384476aa076cf81d35a","source":{"kind":"arxiv","id":"2408.02635","version":2},"attestation_state":"computed","paper":{"title":"Interactive 3D Medical Image Segmentation with SAM 2","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuyun Shen, Wenhao Li, Xiangfeng Wang, Yuhang Shi","submitted_at":"2024-08-05T16:58:56Z","abstract_excerpt":"Interactive medical image segmentation (IMIS) has shown significant potential in enhancing segmentation accuracy by integrating iterative feedback from medical professionals. However, the limited availability of enough 3D medical data restricts the generalization and robustness of most IMIS methods. The Segment Anything Model (SAM), though effective for 2D images, requires expensive semi-auto slice-by-slice annotations for 3D medical images. In this paper, we explore the zero-shot capabilities of SAM 2, the next-generation Meta SAM model trained on videos, for 3D medical image segmentation. By"},"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":"2408.02635","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-08-05T16:58:56Z","cross_cats_sorted":[],"title_canon_sha256":"16eeafbd97b587bc83d368ab94b628c41cfb2764c92753f6a60a70fd809063cd","abstract_canon_sha256":"70f2224ac77751c07e2df70d8e9513fc2db09d388e86c36c44847a66407b1321"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:56:53.441203Z","signature_b64":"mmpuvJzfCW0TFgoth3ovftieC0f0hFl3x94wZ3vgIDib8ktjq7Pe6wcIJY8PxajHQBtVquiMBXJ74udU05dmBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eedf6e4fd658663878376b1d4fbc09326b5861c491317384476aa076cf81d35a","last_reissued_at":"2026-07-05T09:56:53.440730Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:56:53.440730Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Interactive 3D Medical Image Segmentation with SAM 2","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuyun Shen, Wenhao Li, Xiangfeng Wang, Yuhang Shi","submitted_at":"2024-08-05T16:58:56Z","abstract_excerpt":"Interactive medical image segmentation (IMIS) has shown significant potential in enhancing segmentation accuracy by integrating iterative feedback from medical professionals. However, the limited availability of enough 3D medical data restricts the generalization and robustness of most IMIS methods. The Segment Anything Model (SAM), though effective for 2D images, requires expensive semi-auto slice-by-slice annotations for 3D medical images. In this paper, we explore the zero-shot capabilities of SAM 2, the next-generation Meta SAM model trained on videos, for 3D medical image segmentation. By"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.02635","kind":"arxiv","version":2},"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/2408.02635/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":"2408.02635","created_at":"2026-07-05T09:56:53.440788+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.02635v2","created_at":"2026-07-05T09:56:53.440788+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.02635","created_at":"2026-07-05T09:56:53.440788+00:00"},{"alias_kind":"pith_short_12","alias_value":"53PW4T6WLBTD","created_at":"2026-07-05T09:56:53.440788+00:00"},{"alias_kind":"pith_short_16","alias_value":"53PW4T6WLBTDQ6BX","created_at":"2026-07-05T09:56:53.440788+00:00"},{"alias_kind":"pith_short_8","alias_value":"53PW4T6W","created_at":"2026-07-05T09:56:53.440788+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2410.04960","citing_title":"On Efficient Variants of Segment Anything Model: A Survey","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2503.12507","citing_title":"Segment Any-Quality Images with Generative Latent Space Enhancement","ref_index":54,"is_internal_anchor":false},{"citing_arxiv_id":"2505.06907","citing_title":"A Survey on Foundation Models for Personalized Federated Intelligence","ref_index":81,"is_internal_anchor":false},{"citing_arxiv_id":"2601.02018","citing_title":"Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement","ref_index":75,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/53PW4T6WLBTDQ6BXNMOU7PAJGJ","json":"https://pith.science/pith/53PW4T6WLBTDQ6BXNMOU7PAJGJ.json","graph_json":"https://pith.science/api/pith-number/53PW4T6WLBTDQ6BXNMOU7PAJGJ/graph.json","events_json":"https://pith.science/api/pith-number/53PW4T6WLBTDQ6BXNMOU7PAJGJ/events.json","paper":"https://pith.science/paper/53PW4T6W"},"agent_actions":{"view_html":"https://pith.science/pith/53PW4T6WLBTDQ6BXNMOU7PAJGJ","download_json":"https://pith.science/pith/53PW4T6WLBTDQ6BXNMOU7PAJGJ.json","view_paper":"https://pith.science/paper/53PW4T6W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.02635&json=true","fetch_graph":"https://pith.science/api/pith-number/53PW4T6WLBTDQ6BXNMOU7PAJGJ/graph.json","fetch_events":"https://pith.science/api/pith-number/53PW4T6WLBTDQ6BXNMOU7PAJGJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/53PW4T6WLBTDQ6BXNMOU7PAJGJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/53PW4T6WLBTDQ6BXNMOU7PAJGJ/action/storage_attestation","attest_author":"https://pith.science/pith/53PW4T6WLBTDQ6BXNMOU7PAJGJ/action/author_attestation","sign_citation":"https://pith.science/pith/53PW4T6WLBTDQ6BXNMOU7PAJGJ/action/citation_signature","submit_replication":"https://pith.science/pith/53PW4T6WLBTDQ6BXNMOU7PAJGJ/action/replication_record"}},"created_at":"2026-07-05T09:56:53.440788+00:00","updated_at":"2026-07-05T09:56:53.440788+00:00"}