{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:65TGHXEYMOK6TSN76ERVWM2XO3","short_pith_number":"pith:65TGHXEY","schema_version":"1.0","canonical_sha256":"f76663dc986395e9c9bff1235b335776f93996520e75fbd5735b63f79ef8c93c","source":{"kind":"arxiv","id":"1810.03968","version":2},"attestation_state":"computed","paper":{"title":"Improving Myocardium Segmentation in Cardiac CT Angiography using Spectral Information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ivana I\\v{s}gum, Jelmer M. Wolterink, Majd Zreik, Robbert W. van Hamersvelt, Steffen Bruns, Tim Leiner","submitted_at":"2018-09-27T12:34:42Z","abstract_excerpt":"Accurate segmentation of the left ventricle myocardium in cardiac CT angiography (CCTA) is essential for e.g. the assessment of myocardial perfusion. Automatic deep learning methods for segmentation in CCTA might suffer from differences in contrast-agent attenuation between training and test data due to non-standardized contrast administration protocols and varying cardiac output. We propose augmentation of the training data with virtual mono-energetic reconstructions from a spectral CT scanner which show different attenuation levels of the contrast agent. We compare this to an augmentation 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":"1810.03968","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-27T12:34:42Z","cross_cats_sorted":[],"title_canon_sha256":"33d864468566b836c20d6e6b983cbbb8a3523278945924d997c3a0808b15b7c7","abstract_canon_sha256":"70f47efbf15b2994acf018830edf8e262d2f15aabd3921b689d6ff1da7b15059"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:28.857643Z","signature_b64":"GXHxYhu4zi/vCkun2qGU/C/JlDLG5azXDIXSmc2VqUoWI8pv+E6O8gAcRnWMjY67NXmN6dSCqRoRVtUToDNsDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f76663dc986395e9c9bff1235b335776f93996520e75fbd5735b63f79ef8c93c","last_reissued_at":"2026-05-17T23:55:28.857010Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:28.857010Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Myocardium Segmentation in Cardiac CT Angiography using Spectral Information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ivana I\\v{s}gum, Jelmer M. Wolterink, Majd Zreik, Robbert W. van Hamersvelt, Steffen Bruns, Tim Leiner","submitted_at":"2018-09-27T12:34:42Z","abstract_excerpt":"Accurate segmentation of the left ventricle myocardium in cardiac CT angiography (CCTA) is essential for e.g. the assessment of myocardial perfusion. Automatic deep learning methods for segmentation in CCTA might suffer from differences in contrast-agent attenuation between training and test data due to non-standardized contrast administration protocols and varying cardiac output. We propose augmentation of the training data with virtual mono-energetic reconstructions from a spectral CT scanner which show different attenuation levels of the contrast agent. We compare this to an augmentation by"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.03968","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":"1810.03968","created_at":"2026-05-17T23:55:28.857108+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.03968v2","created_at":"2026-05-17T23:55:28.857108+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.03968","created_at":"2026-05-17T23:55:28.857108+00:00"},{"alias_kind":"pith_short_12","alias_value":"65TGHXEYMOK6","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"65TGHXEYMOK6TSN7","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"65TGHXEY","created_at":"2026-05-18T12:32:08.215937+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/65TGHXEYMOK6TSN76ERVWM2XO3","json":"https://pith.science/pith/65TGHXEYMOK6TSN76ERVWM2XO3.json","graph_json":"https://pith.science/api/pith-number/65TGHXEYMOK6TSN76ERVWM2XO3/graph.json","events_json":"https://pith.science/api/pith-number/65TGHXEYMOK6TSN76ERVWM2XO3/events.json","paper":"https://pith.science/paper/65TGHXEY"},"agent_actions":{"view_html":"https://pith.science/pith/65TGHXEYMOK6TSN76ERVWM2XO3","download_json":"https://pith.science/pith/65TGHXEYMOK6TSN76ERVWM2XO3.json","view_paper":"https://pith.science/paper/65TGHXEY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.03968&json=true","fetch_graph":"https://pith.science/api/pith-number/65TGHXEYMOK6TSN76ERVWM2XO3/graph.json","fetch_events":"https://pith.science/api/pith-number/65TGHXEYMOK6TSN76ERVWM2XO3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/65TGHXEYMOK6TSN76ERVWM2XO3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/65TGHXEYMOK6TSN76ERVWM2XO3/action/storage_attestation","attest_author":"https://pith.science/pith/65TGHXEYMOK6TSN76ERVWM2XO3/action/author_attestation","sign_citation":"https://pith.science/pith/65TGHXEYMOK6TSN76ERVWM2XO3/action/citation_signature","submit_replication":"https://pith.science/pith/65TGHXEYMOK6TSN76ERVWM2XO3/action/replication_record"}},"created_at":"2026-05-17T23:55:28.857108+00:00","updated_at":"2026-05-17T23:55:28.857108+00:00"}