{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:PDLRLMAKTMZ2XMPL2LSEGWAXRU","short_pith_number":"pith:PDLRLMAK","canonical_record":{"source":{"id":"1905.02686","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-07T16:43:12Z","cross_cats_sorted":[],"title_canon_sha256":"ba993a33381d75568555c99b7d68ce09c1fe1dbd52cde21361a8d173418d9fc8","abstract_canon_sha256":"555ee60d331db0462f4df2268a54e7c74ff397bf01e46684616e620ebc1475d5"},"schema_version":"1.0"},"canonical_sha256":"78d715b00a9b33abb1ebd2e44358178d035c32c99617f09df09ee4984df6b6bd","source":{"kind":"arxiv","id":"1905.02686","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.02686","created_at":"2026-05-17T23:46:50Z"},{"alias_kind":"arxiv_version","alias_value":"1905.02686v1","created_at":"2026-05-17T23:46:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.02686","created_at":"2026-05-17T23:46:50Z"},{"alias_kind":"pith_short_12","alias_value":"PDLRLMAKTMZ2","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"PDLRLMAKTMZ2XMPL","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"PDLRLMAK","created_at":"2026-05-18T12:33:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:PDLRLMAKTMZ2XMPL2LSEGWAXRU","target":"record","payload":{"canonical_record":{"source":{"id":"1905.02686","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-07T16:43:12Z","cross_cats_sorted":[],"title_canon_sha256":"ba993a33381d75568555c99b7d68ce09c1fe1dbd52cde21361a8d173418d9fc8","abstract_canon_sha256":"555ee60d331db0462f4df2268a54e7c74ff397bf01e46684616e620ebc1475d5"},"schema_version":"1.0"},"canonical_sha256":"78d715b00a9b33abb1ebd2e44358178d035c32c99617f09df09ee4984df6b6bd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:50.734036Z","signature_b64":"XYEEypdZ4v5wpf4G/QOirJQvAgYLVDXcfzT3+Ifwwt3a2WtP7L2moj4mCdDRgEMdwxVWITvWa9/aTjihjpaMAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78d715b00a9b33abb1ebd2e44358178d035c32c99617f09df09ee4984df6b6bd","last_reissued_at":"2026-05-17T23:46:50.733522Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:50.733522Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.02686","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":"2026-05-17T23:46:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z8g/ivUw4U7D5JVZiEHDMaUe2jFwnH5FMzLexk93jqb5JrfwdTYWx60o0oKMjuL5fJpdXFbL5mXVgubpgaXxDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T04:41:56.260499Z"},"content_sha256":"77d55811e15eb60095d4d3d3ee100353eaef986d4768e5fcf12703da96af7497","schema_version":"1.0","event_id":"sha256:77d55811e15eb60095d4d3d3ee100353eaef986d4768e5fcf12703da96af7497"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:PDLRLMAKTMZ2XMPL2LSEGWAXRU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Feature-Fused Context-Encoding Network for Neuroanatomy Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hangfan Liu, Hongming Li, Yong Fan, Yuemeng Li","submitted_at":"2019-05-07T16:43:12Z","abstract_excerpt":"Automatic segmentation of fine-grained brain structures remains a challenging task. Current segmentation methods mainly utilize 2D and 3D deep neural networks. The 2D networks take image slices as input to produce coarse segmentation in less processing time, whereas the 3D networks take the whole image volumes to generated fine-detailed segmentation with more computational burden. In order to obtain accurate fine-grained segmentation efficiently, in this paper, we propose an end-to-end Feature-Fused Context-Encoding Network for brain structure segmentation from MR (magnetic resonance) images. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.02686","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":"2026-05-17T23:46:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qiYQx+YE4HjUi7j0P8/rxDLb139PCcZlqU5/QiGoB+K5AcK1hn3mHeG/mb8z97sMhVQ8449TfwHC57v6HuDHDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T04:41:56.260853Z"},"content_sha256":"ea4cf84856417f6c61726433a14eefacc2bcb6edf4c8256685dea9951004df28","schema_version":"1.0","event_id":"sha256:ea4cf84856417f6c61726433a14eefacc2bcb6edf4c8256685dea9951004df28"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PDLRLMAKTMZ2XMPL2LSEGWAXRU/bundle.json","state_url":"https://pith.science/pith/PDLRLMAKTMZ2XMPL2LSEGWAXRU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PDLRLMAKTMZ2XMPL2LSEGWAXRU/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":"2026-05-30T04:41:56Z","links":{"resolver":"https://pith.science/pith/PDLRLMAKTMZ2XMPL2LSEGWAXRU","bundle":"https://pith.science/pith/PDLRLMAKTMZ2XMPL2LSEGWAXRU/bundle.json","state":"https://pith.science/pith/PDLRLMAKTMZ2XMPL2LSEGWAXRU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PDLRLMAKTMZ2XMPL2LSEGWAXRU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:PDLRLMAKTMZ2XMPL2LSEGWAXRU","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":"555ee60d331db0462f4df2268a54e7c74ff397bf01e46684616e620ebc1475d5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-07T16:43:12Z","title_canon_sha256":"ba993a33381d75568555c99b7d68ce09c1fe1dbd52cde21361a8d173418d9fc8"},"schema_version":"1.0","source":{"id":"1905.02686","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.02686","created_at":"2026-05-17T23:46:50Z"},{"alias_kind":"arxiv_version","alias_value":"1905.02686v1","created_at":"2026-05-17T23:46:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.02686","created_at":"2026-05-17T23:46:50Z"},{"alias_kind":"pith_short_12","alias_value":"PDLRLMAKTMZ2","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"PDLRLMAKTMZ2XMPL","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"PDLRLMAK","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:ea4cf84856417f6c61726433a14eefacc2bcb6edf4c8256685dea9951004df28","target":"graph","created_at":"2026-05-17T23:46:50Z","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":"Automatic segmentation of fine-grained brain structures remains a challenging task. Current segmentation methods mainly utilize 2D and 3D deep neural networks. The 2D networks take image slices as input to produce coarse segmentation in less processing time, whereas the 3D networks take the whole image volumes to generated fine-detailed segmentation with more computational burden. In order to obtain accurate fine-grained segmentation efficiently, in this paper, we propose an end-to-end Feature-Fused Context-Encoding Network for brain structure segmentation from MR (magnetic resonance) images. ","authors_text":"Hangfan Liu, Hongming Li, Yong Fan, Yuemeng Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-07T16:43:12Z","title":"Feature-Fused Context-Encoding Network for Neuroanatomy Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.02686","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":"sha256:77d55811e15eb60095d4d3d3ee100353eaef986d4768e5fcf12703da96af7497","target":"record","created_at":"2026-05-17T23:46:50Z","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":"555ee60d331db0462f4df2268a54e7c74ff397bf01e46684616e620ebc1475d5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-05-07T16:43:12Z","title_canon_sha256":"ba993a33381d75568555c99b7d68ce09c1fe1dbd52cde21361a8d173418d9fc8"},"schema_version":"1.0","source":{"id":"1905.02686","kind":"arxiv","version":1}},"canonical_sha256":"78d715b00a9b33abb1ebd2e44358178d035c32c99617f09df09ee4984df6b6bd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"78d715b00a9b33abb1ebd2e44358178d035c32c99617f09df09ee4984df6b6bd","first_computed_at":"2026-05-17T23:46:50.733522Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:50.733522Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XYEEypdZ4v5wpf4G/QOirJQvAgYLVDXcfzT3+Ifwwt3a2WtP7L2moj4mCdDRgEMdwxVWITvWa9/aTjihjpaMAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:50.734036Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.02686","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:77d55811e15eb60095d4d3d3ee100353eaef986d4768e5fcf12703da96af7497","sha256:ea4cf84856417f6c61726433a14eefacc2bcb6edf4c8256685dea9951004df28"],"state_sha256":"cdfeb6b356a03133e963cbd4d323ac0f8504328df4f6a73109ade7f27dd357e3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PHQ/ocErDm3ZsfcEsYsn22mLcE9FN2Dih+cKG7Fxq4Dwfsuvwb/NYinu4nRkiqvIaR8H1JNiSIbmpAv2pzpjAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T04:41:56.262851Z","bundle_sha256":"d4d5a635182754f9cca0425b7334671c537e7394c16032d2ed06e79087e8965d"}}