{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:ZBNYB3B74Z5A3RQKJUFFVVQGTU","short_pith_number":"pith:ZBNYB3B7","canonical_record":{"source":{"id":"1806.07146","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-19T10:23:15Z","cross_cats_sorted":[],"title_canon_sha256":"72330f8bd06b885517c92aef5c059e83056ac0454d3345266cc4abd39c510728","abstract_canon_sha256":"69d80506b348ec64b3aab073ddd6da1ae6b2eeca61e5ad4060b63a9249918c38"},"schema_version":"1.0"},"canonical_sha256":"c85b80ec3fe67a0dc60a4d0a5ad6069d0d8d96b43dab1362dacbcd325401becd","source":{"kind":"arxiv","id":"1806.07146","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.07146","created_at":"2026-05-18T00:12:51Z"},{"alias_kind":"arxiv_version","alias_value":"1806.07146v1","created_at":"2026-05-18T00:12:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.07146","created_at":"2026-05-18T00:12:51Z"},{"alias_kind":"pith_short_12","alias_value":"ZBNYB3B74Z5A","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"ZBNYB3B74Z5A3RQK","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"ZBNYB3B7","created_at":"2026-05-18T12:33:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:ZBNYB3B74Z5A3RQKJUFFVVQGTU","target":"record","payload":{"canonical_record":{"source":{"id":"1806.07146","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-19T10:23:15Z","cross_cats_sorted":[],"title_canon_sha256":"72330f8bd06b885517c92aef5c059e83056ac0454d3345266cc4abd39c510728","abstract_canon_sha256":"69d80506b348ec64b3aab073ddd6da1ae6b2eeca61e5ad4060b63a9249918c38"},"schema_version":"1.0"},"canonical_sha256":"c85b80ec3fe67a0dc60a4d0a5ad6069d0d8d96b43dab1362dacbcd325401becd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:51.742184Z","signature_b64":"L9ghNzK4uY25e2Mw0YcF5Sm0jW7oXPfrdvcZyY0i2Aw/UIVNn2KF2e72KG4IqkTCCngj2i1ZOWB3inWfgiCLCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c85b80ec3fe67a0dc60a4d0a5ad6069d0d8d96b43dab1362dacbcd325401becd","last_reissued_at":"2026-05-18T00:12:51.741673Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:51.741673Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.07146","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":"2026-05-18T00:12:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Gcx/+MVonENpuXa5z4jHrbUEwVx+YBf0blZ7iUPkYMsVCnSpEyeGbwAnPiqn9/r1251sFOap4adolSmBLOIOAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T19:20:06.685944Z"},"content_sha256":"b0e64db084e0d3066b3a7e1f6d101113e000202b5724c950a2309df0d6e0966f","schema_version":"1.0","event_id":"sha256:b0e64db084e0d3066b3a7e1f6d101113e000202b5724c950a2309df0d6e0966f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:ZBNYB3B74Z5A3RQKJUFFVVQGTU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Automatic segmentation of prostate zones","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Germonda Mooij, Henkjan Huisman, Ines Bagulho","submitted_at":"2018-06-19T10:23:15Z","abstract_excerpt":"Convolutional networks have become state-of-the-art techniques for automatic medical image analysis, with the U-net architecture being the most popular at this moment. In this article we report the application of a 3D version of U-net to the automatic segmentation of prostate peripheral and transition zones in 3D MRI images. Our results are slightly better than recent studies that used 2D U-net and handcrafted feature approaches.\n  In addition, we test ideas for improving the 3D U-net setup, by 1) letting the network segment surrounding tissues, making use of the fixed anatomy, and 2) adjustin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.07146","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":"2026-05-18T00:12:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uutAHUeDHuKa3GGeYuOtR0EE6KBq9f8Q62o6mAWLfLj+DdY8JT3kheD2GCUyLidYQ7gbhPLhJEHpiBm3/uZkCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T19:20:06.687160Z"},"content_sha256":"6dee659676ee24fb89cf3414d068369153b18dea6003937cf71549ad2bff760c","schema_version":"1.0","event_id":"sha256:6dee659676ee24fb89cf3414d068369153b18dea6003937cf71549ad2bff760c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZBNYB3B74Z5A3RQKJUFFVVQGTU/bundle.json","state_url":"https://pith.science/pith/ZBNYB3B74Z5A3RQKJUFFVVQGTU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZBNYB3B74Z5A3RQKJUFFVVQGTU/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":"2026-08-14T19:20:06Z","links":{"resolver":"https://pith.science/pith/ZBNYB3B74Z5A3RQKJUFFVVQGTU","bundle":"https://pith.science/pith/ZBNYB3B74Z5A3RQKJUFFVVQGTU/bundle.json","state":"https://pith.science/pith/ZBNYB3B74Z5A3RQKJUFFVVQGTU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZBNYB3B74Z5A3RQKJUFFVVQGTU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:ZBNYB3B74Z5A3RQKJUFFVVQGTU","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":"69d80506b348ec64b3aab073ddd6da1ae6b2eeca61e5ad4060b63a9249918c38","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-19T10:23:15Z","title_canon_sha256":"72330f8bd06b885517c92aef5c059e83056ac0454d3345266cc4abd39c510728"},"schema_version":"1.0","source":{"id":"1806.07146","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.07146","created_at":"2026-05-18T00:12:51Z"},{"alias_kind":"arxiv_version","alias_value":"1806.07146v1","created_at":"2026-05-18T00:12:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.07146","created_at":"2026-05-18T00:12:51Z"},{"alias_kind":"pith_short_12","alias_value":"ZBNYB3B74Z5A","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"ZBNYB3B74Z5A3RQK","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"ZBNYB3B7","created_at":"2026-05-18T12:33:04Z"}],"graph_snapshots":[{"event_id":"sha256:6dee659676ee24fb89cf3414d068369153b18dea6003937cf71549ad2bff760c","target":"graph","created_at":"2026-05-18T00:12:51Z","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":"Convolutional networks have become state-of-the-art techniques for automatic medical image analysis, with the U-net architecture being the most popular at this moment. In this article we report the application of a 3D version of U-net to the automatic segmentation of prostate peripheral and transition zones in 3D MRI images. Our results are slightly better than recent studies that used 2D U-net and handcrafted feature approaches.\n  In addition, we test ideas for improving the 3D U-net setup, by 1) letting the network segment surrounding tissues, making use of the fixed anatomy, and 2) adjustin","authors_text":"Germonda Mooij, Henkjan Huisman, Ines Bagulho","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-19T10:23:15Z","title":"Automatic segmentation of prostate zones"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.07146","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:b0e64db084e0d3066b3a7e1f6d101113e000202b5724c950a2309df0d6e0966f","target":"record","created_at":"2026-05-18T00:12:51Z","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":"69d80506b348ec64b3aab073ddd6da1ae6b2eeca61e5ad4060b63a9249918c38","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-19T10:23:15Z","title_canon_sha256":"72330f8bd06b885517c92aef5c059e83056ac0454d3345266cc4abd39c510728"},"schema_version":"1.0","source":{"id":"1806.07146","kind":"arxiv","version":1}},"canonical_sha256":"c85b80ec3fe67a0dc60a4d0a5ad6069d0d8d96b43dab1362dacbcd325401becd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c85b80ec3fe67a0dc60a4d0a5ad6069d0d8d96b43dab1362dacbcd325401becd","first_computed_at":"2026-05-18T00:12:51.741673Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:12:51.741673Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"L9ghNzK4uY25e2Mw0YcF5Sm0jW7oXPfrdvcZyY0i2Aw/UIVNn2KF2e72KG4IqkTCCngj2i1ZOWB3inWfgiCLCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:12:51.742184Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.07146","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b0e64db084e0d3066b3a7e1f6d101113e000202b5724c950a2309df0d6e0966f","sha256:6dee659676ee24fb89cf3414d068369153b18dea6003937cf71549ad2bff760c"],"state_sha256":"e571d496a2e19522eaf51ab6c1ffdba48a40c3b61370abafc5f5a54f6cc181dc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xRmr6IThRmWXLYTqdUUkH3uMnE0tvSX9XxkgzYRX1FaAd8LflWIdOiob0tWEPKWksyKLCaFO8tb3rwokzw1qBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T19:20:06.694731Z","bundle_sha256":"dea6cfed92dc5de9d3b4c4743702aa93a80f10af73ae4eea7f4334dd85bbf426"}}