{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:AUSLNAK7GMWKF7PGZYJHABAOS7","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":"ad910375f1fc947350dcd01d3f854052451422c8c22f4bcd9c176bdc8602d497","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-03-17T17:06:00Z","title_canon_sha256":"5d7dd4289fcaaac1bb95b9816df623dadefa1f3df4f3e48f02ce7303a0fba201"},"schema_version":"1.0","source":{"id":"2203.09446","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.09446","created_at":"2026-07-05T04:06:20Z"},{"alias_kind":"arxiv_version","alias_value":"2203.09446v2","created_at":"2026-07-05T04:06:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.09446","created_at":"2026-07-05T04:06:20Z"},{"alias_kind":"pith_short_12","alias_value":"AUSLNAK7GMWK","created_at":"2026-07-05T04:06:20Z"},{"alias_kind":"pith_short_16","alias_value":"AUSLNAK7GMWKF7PG","created_at":"2026-07-05T04:06:20Z"},{"alias_kind":"pith_short_8","alias_value":"AUSLNAK7","created_at":"2026-07-05T04:06:20Z"}],"graph_snapshots":[{"event_id":"sha256:0534aa5b56c9c10fb4dc76420c6155e9633b5d39de8a4579093bec4183070f94","target":"graph","created_at":"2026-07-05T04:06:20Z","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/2203.09446/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The reconstruction of cortical surfaces from brain magnetic resonance imaging (MRI) scans is essential for quantitative analyses of cortical thickness and sulcal morphology. Although traditional and deep learning-based algorithmic pipelines exist for this purpose, they have two major drawbacks: lengthy runtimes of multiple hours (traditional) or intricate post-processing, such as mesh extraction and topology correction (deep learning-based). In this work, we address both of these issues and propose Vox2Cortex, a deep learning-based algorithm that directly yields topologically correct, three-di","authors_text":"Anne-Marie Rickmann, Christian Wachinger, Fabian Bongratz, Sebastian P\\\"olsterl","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-03-17T17:06:00Z","title":"Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D MRI Scans with Geometric Deep Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.09446","kind":"arxiv","version":2},"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:eb5664eea097c226950350050656440dd0ae02c831ac96c00a39eb2ccecde146","target":"record","created_at":"2026-07-05T04:06:20Z","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":"ad910375f1fc947350dcd01d3f854052451422c8c22f4bcd9c176bdc8602d497","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-03-17T17:06:00Z","title_canon_sha256":"5d7dd4289fcaaac1bb95b9816df623dadefa1f3df4f3e48f02ce7303a0fba201"},"schema_version":"1.0","source":{"id":"2203.09446","kind":"arxiv","version":2}},"canonical_sha256":"0524b6815f332ca2fde6ce1270040e97f80533ac3e93e5b1fb809c06fd747d65","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0524b6815f332ca2fde6ce1270040e97f80533ac3e93e5b1fb809c06fd747d65","first_computed_at":"2026-07-05T04:06:20.304359Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:06:20.304359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"W8Rw0/QEICLhyJQ1X+bMA4a6r7648rDDOlM1Ef/IbsM8HsA8OlBDAma/4nMrkejgoA2bl+8l+FXj6nZl0BIvDA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:06:20.304858Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.09446","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eb5664eea097c226950350050656440dd0ae02c831ac96c00a39eb2ccecde146","sha256:0534aa5b56c9c10fb4dc76420c6155e9633b5d39de8a4579093bec4183070f94"],"state_sha256":"8ea5a96ad175ed08eb7b3cfd238c943ec52c92eee43f8cbfb3075f19fc72fb4f"}