{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:6CPLS7VDEBYUUETAJ4LTX4J5HJ","short_pith_number":"pith:6CPLS7VD","canonical_record":{"source":{"id":"1901.11341","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-31T13:10:39Z","cross_cats_sorted":[],"title_canon_sha256":"c84acd15e56003b35b3536760da7da16889232552bc6b97ad85e128293ec121f","abstract_canon_sha256":"e6ee73ae9baa0d930f64268b19bc3e80380f0fdff1dcba5af383f671f7509612"},"schema_version":"1.0"},"canonical_sha256":"f09eb97ea320714a12604f173bf13d3a4b4027aa23d7e8d87924ae8f23e9fc32","source":{"kind":"arxiv","id":"1901.11341","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.11341","created_at":"2026-07-04T23:58:25Z"},{"alias_kind":"arxiv_version","alias_value":"1901.11341v2","created_at":"2026-07-04T23:58:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.11341","created_at":"2026-07-04T23:58:25Z"},{"alias_kind":"pith_short_12","alias_value":"6CPLS7VDEBYU","created_at":"2026-07-04T23:58:25Z"},{"alias_kind":"pith_short_16","alias_value":"6CPLS7VDEBYUUETA","created_at":"2026-07-04T23:58:25Z"},{"alias_kind":"pith_short_8","alias_value":"6CPLS7VD","created_at":"2026-07-04T23:58:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:6CPLS7VDEBYUUETAJ4LTX4J5HJ","target":"record","payload":{"canonical_record":{"source":{"id":"1901.11341","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-31T13:10:39Z","cross_cats_sorted":[],"title_canon_sha256":"c84acd15e56003b35b3536760da7da16889232552bc6b97ad85e128293ec121f","abstract_canon_sha256":"e6ee73ae9baa0d930f64268b19bc3e80380f0fdff1dcba5af383f671f7509612"},"schema_version":"1.0"},"canonical_sha256":"f09eb97ea320714a12604f173bf13d3a4b4027aa23d7e8d87924ae8f23e9fc32","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:58:25.314292Z","signature_b64":"xm9eVsSOtSaQJW/wN1PKn8ks/J4lbnmrN5TrRMUt2vkhy8N5d+amf7k1EEMoAJtOtJ/kcWM016lOBqxZIhpuDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f09eb97ea320714a12604f173bf13d3a4b4027aa23d7e8d87924ae8f23e9fc32","last_reissued_at":"2026-07-04T23:58:25.313724Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:58:25.313724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.11341","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-04T23:58:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D/Fu/SC/xtmRbiKA0dgt9xDznUNoPljKb1lzdTknV/Z1rpDsYsnCaZIkswB7tepL3ZuQKyLVIy6GDzotlPyvCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T14:26:28.183503Z"},"content_sha256":"c242894af8c63207ff1dd5e16227c48b8f8b6f01c98d1a6c7db2e5632e4cc242","schema_version":"1.0","event_id":"sha256:c242894af8c63207ff1dd5e16227c48b8f8b6f01c98d1a6c7db2e5632e4cc242"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:6CPLS7VDEBYUUETAJ4LTX4J5HJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Automated brain extraction of multi-sequence MRI using artificial neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Antje Wick, David Bonekamp, Fabian Isensee, Gianluca Brugnara, Heinz-Peter Schlemmer, Irada Tursunova, Klaus Hermann Maier-Hein, Marianne Schell, Martin Bendszus, Philipp Kickingereder, Sabine Heiland, Ulf Neuberger, Wolfgang Wick","submitted_at":"2019-01-31T13:10:39Z","abstract_excerpt":"Brain extraction is a critical preprocessing step in the analysis of MRI neuroimaging studies and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD-BET) relying on artificial neural networks that aims to overcome these limitations. We demonstrate that HD-BET outperforms six popular, publicly available brain extracti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.11341","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1901.11341/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-04T23:58:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1Z7WK1dnozr/occ2vW40QbnrWDaU3PVhn6VDNsNAuKfObGccL7IiTwCURMKfS8rDYkQWEc5uZl4K/AwQpNgPAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T14:26:28.183878Z"},"content_sha256":"f0979b400f20de8b5d1e46e9ffd3fc2dc7d919fdb0f416d99b04dfc92d279b8f","schema_version":"1.0","event_id":"sha256:f0979b400f20de8b5d1e46e9ffd3fc2dc7d919fdb0f416d99b04dfc92d279b8f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6CPLS7VDEBYUUETAJ4LTX4J5HJ/bundle.json","state_url":"https://pith.science/pith/6CPLS7VDEBYUUETAJ4LTX4J5HJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6CPLS7VDEBYUUETAJ4LTX4J5HJ/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":"2026-07-07T14:26:28Z","links":{"resolver":"https://pith.science/pith/6CPLS7VDEBYUUETAJ4LTX4J5HJ","bundle":"https://pith.science/pith/6CPLS7VDEBYUUETAJ4LTX4J5HJ/bundle.json","state":"https://pith.science/pith/6CPLS7VDEBYUUETAJ4LTX4J5HJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6CPLS7VDEBYUUETAJ4LTX4J5HJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:6CPLS7VDEBYUUETAJ4LTX4J5HJ","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":"e6ee73ae9baa0d930f64268b19bc3e80380f0fdff1dcba5af383f671f7509612","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-31T13:10:39Z","title_canon_sha256":"c84acd15e56003b35b3536760da7da16889232552bc6b97ad85e128293ec121f"},"schema_version":"1.0","source":{"id":"1901.11341","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.11341","created_at":"2026-07-04T23:58:25Z"},{"alias_kind":"arxiv_version","alias_value":"1901.11341v2","created_at":"2026-07-04T23:58:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.11341","created_at":"2026-07-04T23:58:25Z"},{"alias_kind":"pith_short_12","alias_value":"6CPLS7VDEBYU","created_at":"2026-07-04T23:58:25Z"},{"alias_kind":"pith_short_16","alias_value":"6CPLS7VDEBYUUETA","created_at":"2026-07-04T23:58:25Z"},{"alias_kind":"pith_short_8","alias_value":"6CPLS7VD","created_at":"2026-07-04T23:58:25Z"}],"graph_snapshots":[{"event_id":"sha256:f0979b400f20de8b5d1e46e9ffd3fc2dc7d919fdb0f416d99b04dfc92d279b8f","target":"graph","created_at":"2026-07-04T23:58:25Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1901.11341/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Brain extraction is a critical preprocessing step in the analysis of MRI neuroimaging studies and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD-BET) relying on artificial neural networks that aims to overcome these limitations. We demonstrate that HD-BET outperforms six popular, publicly available brain extracti","authors_text":"Antje Wick, David Bonekamp, Fabian Isensee, Gianluca Brugnara, Heinz-Peter Schlemmer, Irada Tursunova, Klaus Hermann Maier-Hein, Marianne Schell, Martin Bendszus, Philipp Kickingereder, Sabine Heiland, Ulf Neuberger, Wolfgang Wick","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-31T13:10:39Z","title":"Automated brain extraction of multi-sequence MRI using artificial neural networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.11341","kind":"arxiv","version":2},"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:c242894af8c63207ff1dd5e16227c48b8f8b6f01c98d1a6c7db2e5632e4cc242","target":"record","created_at":"2026-07-04T23:58:25Z","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":"e6ee73ae9baa0d930f64268b19bc3e80380f0fdff1dcba5af383f671f7509612","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-01-31T13:10:39Z","title_canon_sha256":"c84acd15e56003b35b3536760da7da16889232552bc6b97ad85e128293ec121f"},"schema_version":"1.0","source":{"id":"1901.11341","kind":"arxiv","version":2}},"canonical_sha256":"f09eb97ea320714a12604f173bf13d3a4b4027aa23d7e8d87924ae8f23e9fc32","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f09eb97ea320714a12604f173bf13d3a4b4027aa23d7e8d87924ae8f23e9fc32","first_computed_at":"2026-07-04T23:58:25.313724Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:58:25.313724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xm9eVsSOtSaQJW/wN1PKn8ks/J4lbnmrN5TrRMUt2vkhy8N5d+amf7k1EEMoAJtOtJ/kcWM016lOBqxZIhpuDw==","signature_status":"signed_v1","signed_at":"2026-07-04T23:58:25.314292Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.11341","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c242894af8c63207ff1dd5e16227c48b8f8b6f01c98d1a6c7db2e5632e4cc242","sha256:f0979b400f20de8b5d1e46e9ffd3fc2dc7d919fdb0f416d99b04dfc92d279b8f"],"state_sha256":"82c9d804b59da786c574643400904fabb7c461372f1e8dfbdab656ab0b5c0670"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nmn01Psy3AHYAF0oekCqBEeEvi8BY8wbnAWzJsoNW6tTcsPLJo/tmjajWuqPOE+iqS3tDe03sTjCo6IVTnWyBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T14:26:28.185865Z","bundle_sha256":"bdb9d9db40a283408dc5da72c58919c30fb9da398a1801dc0936ece1ae2d5bdd"}}