{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RN2JX3F4FZEF72I2337H4A7XDO","short_pith_number":"pith:RN2JX3F4","schema_version":"1.0","canonical_sha256":"8b749becbc2e485fe91adefe7e03f71ba6cfc600211692b6667cf163b27a72cf","source":{"kind":"arxiv","id":"2412.12853","version":1},"attestation_state":"computed","paper":{"title":"Automatic Left Ventricular Cavity Segmentation via Deep Spatial Sequential Network in 4D Computed Tomography Studies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"David Dagan Feng, Jinman Kim, Lei Bi, Qian Wang, Ruiyan Zhang, Yuyu Guo, Zhengbin Zhu","submitted_at":"2024-12-17T12:29:32Z","abstract_excerpt":"Automated segmentation of left ventricular cavity (LVC) in temporal cardiac image sequences (multiple time points) is a fundamental requirement for quantitative analysis of its structural and functional changes. Deep learning based methods for the segmentation of LVC are the state of the art; however, these methods are generally formulated to work on single time points, and fails to exploit the complementary information from the temporal image sequences that can aid in segmentation accuracy and consistency among the images across the time points. Furthermore, these segmentation methods perform"},"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":"2412.12853","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-12-17T12:29:32Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"2e8bf01cdb7296a141a73d34b02a2802699f41d6ffd6023bfa5267a7713d3df7","abstract_canon_sha256":"09c9cb397d0525bac51f9424adaa3fee8d4c00e25b309cedfe7c55442ae5aed7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:50:21.633229Z","signature_b64":"sd3tT/MdynKfSdnuqTdfDGVXb/CMbC4ARtTsXLurbECkHE6G8CHrFgczDiW2bgIDGy+h3jRwtDjY4ZkgisdcAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8b749becbc2e485fe91adefe7e03f71ba6cfc600211692b6667cf163b27a72cf","last_reissued_at":"2026-07-05T09:50:21.632842Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:50:21.632842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatic Left Ventricular Cavity Segmentation via Deep Spatial Sequential Network in 4D Computed Tomography Studies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"David Dagan Feng, Jinman Kim, Lei Bi, Qian Wang, Ruiyan Zhang, Yuyu Guo, Zhengbin Zhu","submitted_at":"2024-12-17T12:29:32Z","abstract_excerpt":"Automated segmentation of left ventricular cavity (LVC) in temporal cardiac image sequences (multiple time points) is a fundamental requirement for quantitative analysis of its structural and functional changes. Deep learning based methods for the segmentation of LVC are the state of the art; however, these methods are generally formulated to work on single time points, and fails to exploit the complementary information from the temporal image sequences that can aid in segmentation accuracy and consistency among the images across the time points. Furthermore, these segmentation methods perform"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.12853","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/2412.12853/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":"2412.12853","created_at":"2026-07-05T09:50:21.632898+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.12853v1","created_at":"2026-07-05T09:50:21.632898+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.12853","created_at":"2026-07-05T09:50:21.632898+00:00"},{"alias_kind":"pith_short_12","alias_value":"RN2JX3F4FZEF","created_at":"2026-07-05T09:50:21.632898+00:00"},{"alias_kind":"pith_short_16","alias_value":"RN2JX3F4FZEF72I2","created_at":"2026-07-05T09:50:21.632898+00:00"},{"alias_kind":"pith_short_8","alias_value":"RN2JX3F4","created_at":"2026-07-05T09:50:21.632898+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/RN2JX3F4FZEF72I2337H4A7XDO","json":"https://pith.science/pith/RN2JX3F4FZEF72I2337H4A7XDO.json","graph_json":"https://pith.science/api/pith-number/RN2JX3F4FZEF72I2337H4A7XDO/graph.json","events_json":"https://pith.science/api/pith-number/RN2JX3F4FZEF72I2337H4A7XDO/events.json","paper":"https://pith.science/paper/RN2JX3F4"},"agent_actions":{"view_html":"https://pith.science/pith/RN2JX3F4FZEF72I2337H4A7XDO","download_json":"https://pith.science/pith/RN2JX3F4FZEF72I2337H4A7XDO.json","view_paper":"https://pith.science/paper/RN2JX3F4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.12853&json=true","fetch_graph":"https://pith.science/api/pith-number/RN2JX3F4FZEF72I2337H4A7XDO/graph.json","fetch_events":"https://pith.science/api/pith-number/RN2JX3F4FZEF72I2337H4A7XDO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RN2JX3F4FZEF72I2337H4A7XDO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RN2JX3F4FZEF72I2337H4A7XDO/action/storage_attestation","attest_author":"https://pith.science/pith/RN2JX3F4FZEF72I2337H4A7XDO/action/author_attestation","sign_citation":"https://pith.science/pith/RN2JX3F4FZEF72I2337H4A7XDO/action/citation_signature","submit_replication":"https://pith.science/pith/RN2JX3F4FZEF72I2337H4A7XDO/action/replication_record"}},"created_at":"2026-07-05T09:50:21.632898+00:00","updated_at":"2026-07-05T09:50:21.632898+00:00"}