{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:AERJ4JSTQK4JIVXDRIHXJRR6PO","short_pith_number":"pith:AERJ4JST","schema_version":"1.0","canonical_sha256":"01229e265382b89456e38a0f74c63e7ba06b4688842369a3214fcf58f2f2f83a","source":{"kind":"arxiv","id":"1807.01660","version":2},"attestation_state":"computed","paper":{"title":"Enhancing joint reconstruction and segmentation with non-convex Bregman iteration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.NA","authors_text":"Andi Reci, Andrew J. Sederman, Carola-Bibiane Schoenlieb, Lynn F. Gladden, Martin Benning, Matthias J. Ehrhardt, Richard Mair, Stefanie Reichelt, Veronica Corona","submitted_at":"2018-07-04T16:24:49Z","abstract_excerpt":"All imaging modalities such as computed tomography (CT), emission tomography and magnetic resonance imaging (MRI) require a reconstruction approach to produce an image. A common image processing task for applications that utilise those modalities is image segmentation, typically performed posterior to the reconstruction. We explore a new approach that combines reconstruction and segmentation in a unified framework. We derive a variational model that consists of a total variation regularised reconstruction from undersampled measurements and a Chan-Vese based segmentation. We extend the variatio"},"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":"1807.01660","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-07-04T16:24:49Z","cross_cats_sorted":[],"title_canon_sha256":"6c64916d11b8b6b80d180297c98ccb251cbd7205b9578a1d1f6f578328c8edab","abstract_canon_sha256":"4be7107d258015f3e2def9eebbc20eed7ab70b14bda8ad9569b1a6c09d47ae3e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:37.767518Z","signature_b64":"eJ9OoahkJn5cg8Yj+AoNuG+PSG3Bnz22La/rLHeAkchouQHVMcV5GSA4IDUww2X+eBaSyPYCIlC2shriJlx3Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01229e265382b89456e38a0f74c63e7ba06b4688842369a3214fcf58f2f2f83a","last_reissued_at":"2026-05-17T23:45:37.766941Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:37.766941Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing joint reconstruction and segmentation with non-convex Bregman iteration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.NA","authors_text":"Andi Reci, Andrew J. Sederman, Carola-Bibiane Schoenlieb, Lynn F. Gladden, Martin Benning, Matthias J. Ehrhardt, Richard Mair, Stefanie Reichelt, Veronica Corona","submitted_at":"2018-07-04T16:24:49Z","abstract_excerpt":"All imaging modalities such as computed tomography (CT), emission tomography and magnetic resonance imaging (MRI) require a reconstruction approach to produce an image. A common image processing task for applications that utilise those modalities is image segmentation, typically performed posterior to the reconstruction. We explore a new approach that combines reconstruction and segmentation in a unified framework. We derive a variational model that consists of a total variation regularised reconstruction from undersampled measurements and a Chan-Vese based segmentation. We extend the variatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.01660","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":"1807.01660","created_at":"2026-05-17T23:45:37.767063+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.01660v2","created_at":"2026-05-17T23:45:37.767063+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.01660","created_at":"2026-05-17T23:45:37.767063+00:00"},{"alias_kind":"pith_short_12","alias_value":"AERJ4JSTQK4J","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"AERJ4JSTQK4JIVXD","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"AERJ4JST","created_at":"2026-05-18T12:32:13.499390+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/AERJ4JSTQK4JIVXDRIHXJRR6PO","json":"https://pith.science/pith/AERJ4JSTQK4JIVXDRIHXJRR6PO.json","graph_json":"https://pith.science/api/pith-number/AERJ4JSTQK4JIVXDRIHXJRR6PO/graph.json","events_json":"https://pith.science/api/pith-number/AERJ4JSTQK4JIVXDRIHXJRR6PO/events.json","paper":"https://pith.science/paper/AERJ4JST"},"agent_actions":{"view_html":"https://pith.science/pith/AERJ4JSTQK4JIVXDRIHXJRR6PO","download_json":"https://pith.science/pith/AERJ4JSTQK4JIVXDRIHXJRR6PO.json","view_paper":"https://pith.science/paper/AERJ4JST","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.01660&json=true","fetch_graph":"https://pith.science/api/pith-number/AERJ4JSTQK4JIVXDRIHXJRR6PO/graph.json","fetch_events":"https://pith.science/api/pith-number/AERJ4JSTQK4JIVXDRIHXJRR6PO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AERJ4JSTQK4JIVXDRIHXJRR6PO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AERJ4JSTQK4JIVXDRIHXJRR6PO/action/storage_attestation","attest_author":"https://pith.science/pith/AERJ4JSTQK4JIVXDRIHXJRR6PO/action/author_attestation","sign_citation":"https://pith.science/pith/AERJ4JSTQK4JIVXDRIHXJRR6PO/action/citation_signature","submit_replication":"https://pith.science/pith/AERJ4JSTQK4JIVXDRIHXJRR6PO/action/replication_record"}},"created_at":"2026-05-17T23:45:37.767063+00:00","updated_at":"2026-05-17T23:45:37.767063+00:00"}