{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:X7HDCO7NRYO3D6NAO7MXBQSCET","short_pith_number":"pith:X7HDCO7N","canonical_record":{"source":{"id":"1705.08302","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2017-05-22T12:32:25Z","cross_cats_sorted":[],"title_canon_sha256":"c8339299b6008ef7d3ca648b5ea9e43dd7920cf51565dd8d9448087570307524","abstract_canon_sha256":"9b332c81198331d1200a2e596e06036ad984956d8b5adf79a1800a8c3360cc18"},"schema_version":"1.0"},"canonical_sha256":"bfce313bed8e1db1f9a077d970c24224f0a85ad0a94480c90bc988d6df695e5c","source":{"kind":"arxiv","id":"1705.08302","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.08302","created_at":"2026-05-18T00:28:40Z"},{"alias_kind":"arxiv_version","alias_value":"1705.08302v4","created_at":"2026-05-18T00:28:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.08302","created_at":"2026-05-18T00:28:40Z"},{"alias_kind":"pith_short_12","alias_value":"X7HDCO7NRYO3","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"X7HDCO7NRYO3D6NA","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"X7HDCO7N","created_at":"2026-05-18T12:31:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:X7HDCO7NRYO3D6NAO7MXBQSCET","target":"record","payload":{"canonical_record":{"source":{"id":"1705.08302","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2017-05-22T12:32:25Z","cross_cats_sorted":[],"title_canon_sha256":"c8339299b6008ef7d3ca648b5ea9e43dd7920cf51565dd8d9448087570307524","abstract_canon_sha256":"9b332c81198331d1200a2e596e06036ad984956d8b5adf79a1800a8c3360cc18"},"schema_version":"1.0"},"canonical_sha256":"bfce313bed8e1db1f9a077d970c24224f0a85ad0a94480c90bc988d6df695e5c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:40.787714Z","signature_b64":"AfMOIXr1b02cEsX8C5gkVc7mzK+nudt6NcK933R1fAkOgEwQSdVew4ZgAcO53yqJNVpHoeBQHymrUiLLaBYEBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bfce313bed8e1db1f9a077d970c24224f0a85ad0a94480c90bc988d6df695e5c","last_reissued_at":"2026-05-18T00:28:40.787044Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:40.787044Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.08302","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:28:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FkF+3kvVBAI4feREjzgT+DvNedVlx3sPvQ4mK861sl7rcLXoVlBrTz14eO5UArmUxRdyTZz6qpeDeQCaO29mAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T13:05:09.970162Z"},"content_sha256":"3fe725ecb778afd96cf41c79a24dc841a30c71cfd719f820872584703e5410cc","schema_version":"1.0","event_id":"sha256:3fe725ecb778afd96cf41c79a24dc841a30c71cfd719f820872584703e5410cc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:X7HDCO7NRYO3D6NAO7MXBQSCET","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Antonio de Marvao, Ben Glocker, Bernhard Kainz, Daniel Rueckert, Declan O'Regan, Enzo Ferrante, Jose Caballero, Konstantinos Kamnitsas, Mattias Heinrich, Ozan Oktay, Stuart Cook, Timothy Dawes, Wenjia Bai","submitted_at":"2017-05-22T12:32:25Z","abstract_excerpt":"Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning based techniques. However, in most recent and promising techniques such as CNN based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.08302","kind":"arxiv","version":4},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:28:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cUO9v8KVklo1gtW6TAcnxHhxtU6CiHp7nDEDO2AY3ajp87/6d+jQlAGFvO9GvZuRQIFAl1OltmT9G4AZLRDfAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T13:05:09.970795Z"},"content_sha256":"3ed3eda34e1f8ad28d4c3b064b1aaf6742a7dc99def71a5aa8c35f3dbe858559","schema_version":"1.0","event_id":"sha256:3ed3eda34e1f8ad28d4c3b064b1aaf6742a7dc99def71a5aa8c35f3dbe858559"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X7HDCO7NRYO3D6NAO7MXBQSCET/bundle.json","state_url":"https://pith.science/pith/X7HDCO7NRYO3D6NAO7MXBQSCET/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X7HDCO7NRYO3D6NAO7MXBQSCET/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-06T13:05:09Z","links":{"resolver":"https://pith.science/pith/X7HDCO7NRYO3D6NAO7MXBQSCET","bundle":"https://pith.science/pith/X7HDCO7NRYO3D6NAO7MXBQSCET/bundle.json","state":"https://pith.science/pith/X7HDCO7NRYO3D6NAO7MXBQSCET/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X7HDCO7NRYO3D6NAO7MXBQSCET/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:X7HDCO7NRYO3D6NAO7MXBQSCET","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9b332c81198331d1200a2e596e06036ad984956d8b5adf79a1800a8c3360cc18","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2017-05-22T12:32:25Z","title_canon_sha256":"c8339299b6008ef7d3ca648b5ea9e43dd7920cf51565dd8d9448087570307524"},"schema_version":"1.0","source":{"id":"1705.08302","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.08302","created_at":"2026-05-18T00:28:40Z"},{"alias_kind":"arxiv_version","alias_value":"1705.08302v4","created_at":"2026-05-18T00:28:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.08302","created_at":"2026-05-18T00:28:40Z"},{"alias_kind":"pith_short_12","alias_value":"X7HDCO7NRYO3","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"X7HDCO7NRYO3D6NA","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"X7HDCO7N","created_at":"2026-05-18T12:31:53Z"}],"graph_snapshots":[{"event_id":"sha256:3ed3eda34e1f8ad28d4c3b064b1aaf6742a7dc99def71a5aa8c35f3dbe858559","target":"graph","created_at":"2026-05-18T00:28:40Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning based techniques. However, in most recent and promising techniques such as CNN based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not i","authors_text":"Antonio de Marvao, Ben Glocker, Bernhard Kainz, Daniel Rueckert, Declan O'Regan, Enzo Ferrante, Jose Caballero, Konstantinos Kamnitsas, Mattias Heinrich, Ozan Oktay, Stuart Cook, Timothy Dawes, Wenjia Bai","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2017-05-22T12:32:25Z","title":"Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.08302","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3fe725ecb778afd96cf41c79a24dc841a30c71cfd719f820872584703e5410cc","target":"record","created_at":"2026-05-18T00:28:40Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9b332c81198331d1200a2e596e06036ad984956d8b5adf79a1800a8c3360cc18","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2017-05-22T12:32:25Z","title_canon_sha256":"c8339299b6008ef7d3ca648b5ea9e43dd7920cf51565dd8d9448087570307524"},"schema_version":"1.0","source":{"id":"1705.08302","kind":"arxiv","version":4}},"canonical_sha256":"bfce313bed8e1db1f9a077d970c24224f0a85ad0a94480c90bc988d6df695e5c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bfce313bed8e1db1f9a077d970c24224f0a85ad0a94480c90bc988d6df695e5c","first_computed_at":"2026-05-18T00:28:40.787044Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:28:40.787044Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AfMOIXr1b02cEsX8C5gkVc7mzK+nudt6NcK933R1fAkOgEwQSdVew4ZgAcO53yqJNVpHoeBQHymrUiLLaBYEBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:28:40.787714Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.08302","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3fe725ecb778afd96cf41c79a24dc841a30c71cfd719f820872584703e5410cc","sha256:3ed3eda34e1f8ad28d4c3b064b1aaf6742a7dc99def71a5aa8c35f3dbe858559"],"state_sha256":"329249aa6b398647da1157ce0662db96b6c14591c501ae4736536ce6b7f6a811"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iSIjTbvUCbUO4G0mFkCFftqObBacG/kRRKZydAHRsuT7Gw5PT6olLBuj12O6JIrRiVX2EhW1gtqpilPfvpoABg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T13:05:09.973691Z","bundle_sha256":"d1f0b29c3ce515bc89e94b93ec98f5e3efe9b058605877a0220dd482ee58b8d1"}}