{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3A3F372UNOZDCTAE53IXZSRP6Q","short_pith_number":"pith:3A3F372U","schema_version":"1.0","canonical_sha256":"d8365dff546bb2314c04eed17cca2ff43b2405c62d95835b4475ba84a00003cd","source":{"kind":"arxiv","id":"2307.09000","version":1},"attestation_state":"computed","paper":{"title":"TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexandra J. Golby, Chaoyi Zhang, Fan Zhang, Lauren J. O'Donnell, Nikos Makris, Tengfei Xue, Weidong Cai, Yogesh Rathi, Yuqian Chen","submitted_at":"2023-07-18T06:35:12Z","abstract_excerpt":"Diffusion MRI tractography parcellation classifies streamlines into anatomical fiber tracts to enable quantification and visualization for clinical and scientific applications. Current tractography parcellation methods rely heavily on registration, but registration inaccuracies can affect parcellation and the computational cost of registration is high for large-scale datasets. Recently, deep-learning-based methods have been proposed for tractography parcellation using various types of representations for streamlines. However, these methods only focus on the information from a single streamline"},"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":"2307.09000","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-07-18T06:35:12Z","cross_cats_sorted":[],"title_canon_sha256":"c04dfc6f835e40c56876c8415c8403203d0661e1710210510d682520edd00cb2","abstract_canon_sha256":"356e943a58069470858b784350bf76d5967ee4a3713e40258c9c8e3bb08a4635"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:31:37.581197Z","signature_b64":"KZnM7z34R7dMhCLzCPVpSC+pLFC+5M5rFIgkoiwcFPazNyWxbdLsyddnrpohU/f4csQS+JEKSxGLdHtYPp1pDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d8365dff546bb2314c04eed17cca2ff43b2405c62d95835b4475ba84a00003cd","last_reissued_at":"2026-07-05T06:31:37.580724Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:31:37.580724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexandra J. Golby, Chaoyi Zhang, Fan Zhang, Lauren J. O'Donnell, Nikos Makris, Tengfei Xue, Weidong Cai, Yogesh Rathi, Yuqian Chen","submitted_at":"2023-07-18T06:35:12Z","abstract_excerpt":"Diffusion MRI tractography parcellation classifies streamlines into anatomical fiber tracts to enable quantification and visualization for clinical and scientific applications. Current tractography parcellation methods rely heavily on registration, but registration inaccuracies can affect parcellation and the computational cost of registration is high for large-scale datasets. Recently, deep-learning-based methods have been proposed for tractography parcellation using various types of representations for streamlines. However, these methods only focus on the information from a single streamline"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.09000","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/2307.09000/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":"2307.09000","created_at":"2026-07-05T06:31:37.580782+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.09000v1","created_at":"2026-07-05T06:31:37.580782+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.09000","created_at":"2026-07-05T06:31:37.580782+00:00"},{"alias_kind":"pith_short_12","alias_value":"3A3F372UNOZD","created_at":"2026-07-05T06:31:37.580782+00:00"},{"alias_kind":"pith_short_16","alias_value":"3A3F372UNOZDCTAE","created_at":"2026-07-05T06:31:37.580782+00:00"},{"alias_kind":"pith_short_8","alias_value":"3A3F372U","created_at":"2026-07-05T06:31:37.580782+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/3A3F372UNOZDCTAE53IXZSRP6Q","json":"https://pith.science/pith/3A3F372UNOZDCTAE53IXZSRP6Q.json","graph_json":"https://pith.science/api/pith-number/3A3F372UNOZDCTAE53IXZSRP6Q/graph.json","events_json":"https://pith.science/api/pith-number/3A3F372UNOZDCTAE53IXZSRP6Q/events.json","paper":"https://pith.science/paper/3A3F372U"},"agent_actions":{"view_html":"https://pith.science/pith/3A3F372UNOZDCTAE53IXZSRP6Q","download_json":"https://pith.science/pith/3A3F372UNOZDCTAE53IXZSRP6Q.json","view_paper":"https://pith.science/paper/3A3F372U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.09000&json=true","fetch_graph":"https://pith.science/api/pith-number/3A3F372UNOZDCTAE53IXZSRP6Q/graph.json","fetch_events":"https://pith.science/api/pith-number/3A3F372UNOZDCTAE53IXZSRP6Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3A3F372UNOZDCTAE53IXZSRP6Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3A3F372UNOZDCTAE53IXZSRP6Q/action/storage_attestation","attest_author":"https://pith.science/pith/3A3F372UNOZDCTAE53IXZSRP6Q/action/author_attestation","sign_citation":"https://pith.science/pith/3A3F372UNOZDCTAE53IXZSRP6Q/action/citation_signature","submit_replication":"https://pith.science/pith/3A3F372UNOZDCTAE53IXZSRP6Q/action/replication_record"}},"created_at":"2026-07-05T06:31:37.580782+00:00","updated_at":"2026-07-05T06:31:37.580782+00:00"}