{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:PZADNYYNVY5UBJSH2EILMKH6UH","short_pith_number":"pith:PZADNYYN","canonical_record":{"source":{"id":"1907.11483","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-07-26T11:08:32Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"7f6771963d48f9b841297659165ef9afe6b8ca52509dd152e7e66f6e840e299a","abstract_canon_sha256":"7147c1bb19220f05e5375747c1c23bf8d555cc49be740145049e3bf036336018"},"schema_version":"1.0"},"canonical_sha256":"7e4036e30dae3b40a647d110b628fea1fd6f9e69480e19620b7dbe781ca975a1","source":{"kind":"arxiv","id":"1907.11483","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.11483","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"arxiv_version","alias_value":"1907.11483v1","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.11483","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"pith_short_12","alias_value":"PZADNYYNVY5U","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"PZADNYYNVY5UBJSH","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"PZADNYYN","created_at":"2026-05-18T12:33:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:PZADNYYNVY5UBJSH2EILMKH6UH","target":"record","payload":{"canonical_record":{"source":{"id":"1907.11483","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-07-26T11:08:32Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"7f6771963d48f9b841297659165ef9afe6b8ca52509dd152e7e66f6e840e299a","abstract_canon_sha256":"7147c1bb19220f05e5375747c1c23bf8d555cc49be740145049e3bf036336018"},"schema_version":"1.0"},"canonical_sha256":"7e4036e30dae3b40a647d110b628fea1fd6f9e69480e19620b7dbe781ca975a1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:28.962354Z","signature_b64":"caj10amiRblr3qj/trwSIYnFzxv5llFGkKLK3OO20FTMNwPeURYji4rAp4St+zGd+uMdtQNaBkOVzoStNiEtAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e4036e30dae3b40a647d110b628fea1fd6f9e69480e19620b7dbe781ca975a1","last_reissued_at":"2026-05-17T23:39:28.961711Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:28.961711Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.11483","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MLzPpT7Hco7UKJmLX7zUHYeC7OXCvBsRWTqOUTycUUoxB6HIxg5tukdfXalZk1AyQF7AoAc6dlwXLTXtpMJSAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T15:16:12.181517Z"},"content_sha256":"3abf89c4505b694652300df2e91e7ed461dcaaba88c870fe337e7e526c5230eb","schema_version":"1.0","event_id":"sha256:3abf89c4505b694652300df2e91e7ed461dcaaba88c870fe337e7e526c5230eb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:PZADNYYNVY5UBJSH2EILMKH6UH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Bin Dong, Fan Yang, Fei Yu, Jie Zhao, Li Zhang, Quanzheng Li, Yanjun Gong, Yuxi Li, Zhi Wang","submitted_at":"2019-07-26T11:08:32Z","abstract_excerpt":"Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual annotation which is often time-consuming and can some time be infeasible. To solve this problem, we propose a knowledge transfer based shape-consistent generative adversarial network (SC-GAN), which is an annotation-free approach that uses the knowledge from publicly available annotated fundus dataset to segment coronary arteries. The proposed network is trained in an end-to-end fashion, generating and segmenting synthe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.11483","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U7sptknqhmstvQemDsF4BAALGy1ZGU1+H/Su8lmlorAsDRGY3YsWgDHL7SARFPCkYW/b0wCpbZP0g7MHOYmSBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T15:16:12.181963Z"},"content_sha256":"0a3021d091055181145237eb3b52b0486bb5726c057b2eb795699bcbb2bf0775","schema_version":"1.0","event_id":"sha256:0a3021d091055181145237eb3b52b0486bb5726c057b2eb795699bcbb2bf0775"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PZADNYYNVY5UBJSH2EILMKH6UH/bundle.json","state_url":"https://pith.science/pith/PZADNYYNVY5UBJSH2EILMKH6UH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PZADNYYNVY5UBJSH2EILMKH6UH/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":"2026-05-31T15:16:12Z","links":{"resolver":"https://pith.science/pith/PZADNYYNVY5UBJSH2EILMKH6UH","bundle":"https://pith.science/pith/PZADNYYNVY5UBJSH2EILMKH6UH/bundle.json","state":"https://pith.science/pith/PZADNYYNVY5UBJSH2EILMKH6UH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PZADNYYNVY5UBJSH2EILMKH6UH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:PZADNYYNVY5UBJSH2EILMKH6UH","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":"7147c1bb19220f05e5375747c1c23bf8d555cc49be740145049e3bf036336018","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-07-26T11:08:32Z","title_canon_sha256":"7f6771963d48f9b841297659165ef9afe6b8ca52509dd152e7e66f6e840e299a"},"schema_version":"1.0","source":{"id":"1907.11483","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.11483","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"arxiv_version","alias_value":"1907.11483v1","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.11483","created_at":"2026-05-17T23:39:28Z"},{"alias_kind":"pith_short_12","alias_value":"PZADNYYNVY5U","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"PZADNYYNVY5UBJSH","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"PZADNYYN","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:0a3021d091055181145237eb3b52b0486bb5726c057b2eb795699bcbb2bf0775","target":"graph","created_at":"2026-05-17T23:39:28Z","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":"Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual annotation which is often time-consuming and can some time be infeasible. To solve this problem, we propose a knowledge transfer based shape-consistent generative adversarial network (SC-GAN), which is an annotation-free approach that uses the knowledge from publicly available annotated fundus dataset to segment coronary arteries. The proposed network is trained in an end-to-end fashion, generating and segmenting synthe","authors_text":"Bin Dong, Fan Yang, Fei Yu, Jie Zhao, Li Zhang, Quanzheng Li, Yanjun Gong, Yuxi Li, Zhi Wang","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-07-26T11:08:32Z","title":"Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.11483","kind":"arxiv","version":1},"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":"sha256:3abf89c4505b694652300df2e91e7ed461dcaaba88c870fe337e7e526c5230eb","target":"record","created_at":"2026-05-17T23:39:28Z","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":"7147c1bb19220f05e5375747c1c23bf8d555cc49be740145049e3bf036336018","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-07-26T11:08:32Z","title_canon_sha256":"7f6771963d48f9b841297659165ef9afe6b8ca52509dd152e7e66f6e840e299a"},"schema_version":"1.0","source":{"id":"1907.11483","kind":"arxiv","version":1}},"canonical_sha256":"7e4036e30dae3b40a647d110b628fea1fd6f9e69480e19620b7dbe781ca975a1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7e4036e30dae3b40a647d110b628fea1fd6f9e69480e19620b7dbe781ca975a1","first_computed_at":"2026-05-17T23:39:28.961711Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:28.961711Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"caj10amiRblr3qj/trwSIYnFzxv5llFGkKLK3OO20FTMNwPeURYji4rAp4St+zGd+uMdtQNaBkOVzoStNiEtAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:28.962354Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.11483","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3abf89c4505b694652300df2e91e7ed461dcaaba88c870fe337e7e526c5230eb","sha256:0a3021d091055181145237eb3b52b0486bb5726c057b2eb795699bcbb2bf0775"],"state_sha256":"a9fcd151233d241592493081fe9770230e13d62a312dc83772c586091b9bb402"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RXXbryM9phLm3+ZbW3wqL1TG0yGnXYGpUKHET2xX2hRYteIweXMhHkvKaYRToHzG7f60JgRfPexGLvtmZAATBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T15:16:12.185833Z","bundle_sha256":"77fb8bb0119ba4eaa7b17bb4f9a0de1d49005ce6a0a2d4ff881de2108b2cdd21"}}