{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:SBIL6LNMIKXPPPPB5DBUGIMXQ4","short_pith_number":"pith:SBIL6LNM","schema_version":"1.0","canonical_sha256":"9050bf2dac42aef7bde1e8c343219787381c21016da201716f94c8461e599c87","source":{"kind":"arxiv","id":"1903.06260","version":1},"attestation_state":"computed","paper":{"title":"Mixture Modeling of Global Shape Priors and Autoencoding Local Intensity Priors for Left Atrium Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CG","cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Joshua Cates, Nassir Marrouche, Riddhish Bhalodia, Ross Whitaker, Shireen Elhabian, Tim Sodergren","submitted_at":"2019-03-06T23:24:08Z","abstract_excerpt":"Difficult image segmentation problems, for instance left atrium MRI, can be addressed by incorporating shape priors to find solutions that are consistent with known objects. Nonetheless, a single multivariate Gaussian is not an adequate model in cases with significant nonlinear shape variation or where the prior distribution is multimodal. Nonparametric density estimation is more general, but has a ravenous appetite for training samples and poses serious challenges in optimization, especially in high dimensional spaces. Here, we propose a maximum-a-posteriori formulation that relies on a gener"},"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":"1903.06260","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-06T23:24:08Z","cross_cats_sorted":["cs.CG","cs.LG","stat.ML"],"title_canon_sha256":"165dabf53b143f2e71f718488cb1354c30da2b7ef02747d36c4571f38cc25be0","abstract_canon_sha256":"edd04290de65bd2b60e6c2223a96fbc73fa48f86d12edbf886e976466150c34c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:11.323666Z","signature_b64":"d0OVET9gZ7unP4T+zrOJXONSh1YzXCr+Y57PTKmhTdkUrBiQYRS1w8nzV/CrePZykd2+no3ZLO3Numa8bTUKDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9050bf2dac42aef7bde1e8c343219787381c21016da201716f94c8461e599c87","last_reissued_at":"2026-05-17T23:51:11.323166Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:11.323166Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mixture Modeling of Global Shape Priors and Autoencoding Local Intensity Priors for Left Atrium Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CG","cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Joshua Cates, Nassir Marrouche, Riddhish Bhalodia, Ross Whitaker, Shireen Elhabian, Tim Sodergren","submitted_at":"2019-03-06T23:24:08Z","abstract_excerpt":"Difficult image segmentation problems, for instance left atrium MRI, can be addressed by incorporating shape priors to find solutions that are consistent with known objects. Nonetheless, a single multivariate Gaussian is not an adequate model in cases with significant nonlinear shape variation or where the prior distribution is multimodal. Nonparametric density estimation is more general, but has a ravenous appetite for training samples and poses serious challenges in optimization, especially in high dimensional spaces. Here, we propose a maximum-a-posteriori formulation that relies on a gener"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.06260","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":""},"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":"1903.06260","created_at":"2026-05-17T23:51:11.323242+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.06260v1","created_at":"2026-05-17T23:51:11.323242+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.06260","created_at":"2026-05-17T23:51:11.323242+00:00"},{"alias_kind":"pith_short_12","alias_value":"SBIL6LNMIKXP","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"SBIL6LNMIKXPPPPB","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"SBIL6LNM","created_at":"2026-05-18T12:33:27.125529+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/SBIL6LNMIKXPPPPB5DBUGIMXQ4","json":"https://pith.science/pith/SBIL6LNMIKXPPPPB5DBUGIMXQ4.json","graph_json":"https://pith.science/api/pith-number/SBIL6LNMIKXPPPPB5DBUGIMXQ4/graph.json","events_json":"https://pith.science/api/pith-number/SBIL6LNMIKXPPPPB5DBUGIMXQ4/events.json","paper":"https://pith.science/paper/SBIL6LNM"},"agent_actions":{"view_html":"https://pith.science/pith/SBIL6LNMIKXPPPPB5DBUGIMXQ4","download_json":"https://pith.science/pith/SBIL6LNMIKXPPPPB5DBUGIMXQ4.json","view_paper":"https://pith.science/paper/SBIL6LNM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.06260&json=true","fetch_graph":"https://pith.science/api/pith-number/SBIL6LNMIKXPPPPB5DBUGIMXQ4/graph.json","fetch_events":"https://pith.science/api/pith-number/SBIL6LNMIKXPPPPB5DBUGIMXQ4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SBIL6LNMIKXPPPPB5DBUGIMXQ4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SBIL6LNMIKXPPPPB5DBUGIMXQ4/action/storage_attestation","attest_author":"https://pith.science/pith/SBIL6LNMIKXPPPPB5DBUGIMXQ4/action/author_attestation","sign_citation":"https://pith.science/pith/SBIL6LNMIKXPPPPB5DBUGIMXQ4/action/citation_signature","submit_replication":"https://pith.science/pith/SBIL6LNMIKXPPPPB5DBUGIMXQ4/action/replication_record"}},"created_at":"2026-05-17T23:51:11.323242+00:00","updated_at":"2026-05-17T23:51:11.323242+00:00"}