{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:AMOJP5WXHMMXRRG6KJGVO2WXQL","short_pith_number":"pith:AMOJP5WX","schema_version":"1.0","canonical_sha256":"031c97f6d73b1978c4de524d576ad782f7b5684ae7729d0998f41bd0d5b746e4","source":{"kind":"arxiv","id":"1507.07508","version":2},"attestation_state":"computed","paper":{"title":"Fast Segmentation of Left Ventricle in CT Images by Explicit Shape Regression using Random Pixel Difference Features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Devon Lundine, Guanglei Xiong, Haoyin Zhou, James K. Min, Peng Sun","submitted_at":"2015-07-27T18:17:55Z","abstract_excerpt":"Recently, machine learning has been successfully applied to model-based left ventricle (LV) segmentation. The general framework involves two stages, which starts with LV localization and is followed by boundary delineation. Both are driven by supervised learning techniques. When compared to previous non-learning-based methods, several advantages have been shown, including full automation and improved accuracy. However, the speed is still slow, in the order of several seconds, for applications involving a large number of cases or case loads requiring real-time performance. In this paper, we pro"},"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":"1507.07508","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-07-27T18:17:55Z","cross_cats_sorted":[],"title_canon_sha256":"5a67905d53148151788d812f4bfe14840d3ed5f63a8a297b6b49fe2cafdb5c4b","abstract_canon_sha256":"c0db99db3321ed60bb6e99483a1972293d3e441f897aa554bbf7484a1172c203"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:36:12.431386Z","signature_b64":"dmlTzOf7JHUH280PkmTN0YLOXR134eDT+eB+EWv/prBGmUv7zTvfxB1AHoOgdVm+iC57vfYCL3Ot08Ab4BbbCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"031c97f6d73b1978c4de524d576ad782f7b5684ae7729d0998f41bd0d5b746e4","last_reissued_at":"2026-05-18T01:36:12.430916Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:36:12.430916Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast Segmentation of Left Ventricle in CT Images by Explicit Shape Regression using Random Pixel Difference Features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Devon Lundine, Guanglei Xiong, Haoyin Zhou, James K. Min, Peng Sun","submitted_at":"2015-07-27T18:17:55Z","abstract_excerpt":"Recently, machine learning has been successfully applied to model-based left ventricle (LV) segmentation. The general framework involves two stages, which starts with LV localization and is followed by boundary delineation. Both are driven by supervised learning techniques. When compared to previous non-learning-based methods, several advantages have been shown, including full automation and improved accuracy. However, the speed is still slow, in the order of several seconds, for applications involving a large number of cases or case loads requiring real-time performance. In this paper, we pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.07508","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":""},"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":"1507.07508","created_at":"2026-05-18T01:36:12.430988+00:00"},{"alias_kind":"arxiv_version","alias_value":"1507.07508v2","created_at":"2026-05-18T01:36:12.430988+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.07508","created_at":"2026-05-18T01:36:12.430988+00:00"},{"alias_kind":"pith_short_12","alias_value":"AMOJP5WXHMMX","created_at":"2026-05-18T12:29:10.953037+00:00"},{"alias_kind":"pith_short_16","alias_value":"AMOJP5WXHMMXRRG6","created_at":"2026-05-18T12:29:10.953037+00:00"},{"alias_kind":"pith_short_8","alias_value":"AMOJP5WX","created_at":"2026-05-18T12:29:10.953037+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/AMOJP5WXHMMXRRG6KJGVO2WXQL","json":"https://pith.science/pith/AMOJP5WXHMMXRRG6KJGVO2WXQL.json","graph_json":"https://pith.science/api/pith-number/AMOJP5WXHMMXRRG6KJGVO2WXQL/graph.json","events_json":"https://pith.science/api/pith-number/AMOJP5WXHMMXRRG6KJGVO2WXQL/events.json","paper":"https://pith.science/paper/AMOJP5WX"},"agent_actions":{"view_html":"https://pith.science/pith/AMOJP5WXHMMXRRG6KJGVO2WXQL","download_json":"https://pith.science/pith/AMOJP5WXHMMXRRG6KJGVO2WXQL.json","view_paper":"https://pith.science/paper/AMOJP5WX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1507.07508&json=true","fetch_graph":"https://pith.science/api/pith-number/AMOJP5WXHMMXRRG6KJGVO2WXQL/graph.json","fetch_events":"https://pith.science/api/pith-number/AMOJP5WXHMMXRRG6KJGVO2WXQL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AMOJP5WXHMMXRRG6KJGVO2WXQL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AMOJP5WXHMMXRRG6KJGVO2WXQL/action/storage_attestation","attest_author":"https://pith.science/pith/AMOJP5WXHMMXRRG6KJGVO2WXQL/action/author_attestation","sign_citation":"https://pith.science/pith/AMOJP5WXHMMXRRG6KJGVO2WXQL/action/citation_signature","submit_replication":"https://pith.science/pith/AMOJP5WXHMMXRRG6KJGVO2WXQL/action/replication_record"}},"created_at":"2026-05-18T01:36:12.430988+00:00","updated_at":"2026-05-18T01:36:12.430988+00:00"}