{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:XM2N5SHB5ZYXK3RBB4O4C34OKF","short_pith_number":"pith:XM2N5SHB","schema_version":"1.0","canonical_sha256":"bb34dec8e1ee71756e210f1dc16f8e516a45db13faa8ed364886d865c52f1700","source":{"kind":"arxiv","id":"1709.02967","version":1},"attestation_state":"computed","paper":{"title":"Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrew Beers, Bruce Rosen, CP Mammen, Elizabeth Gerstner, Emmett Sartor, James Brown, Jayashree Kalpathy-Cramer, Ken Chang","submitted_at":"2017-09-09T15:57:51Z","abstract_excerpt":"Deep learning has quickly become the weapon of choice for brain lesion segmentation. However, few existing algorithms pre-configure any biological context of their chosen segmentation tissues, and instead rely on the neural network's optimizer to develop such associations de novo. We present a novel method for applying deep neural networks to the problem of glioma tissue segmentation that takes into account the structured nature of gliomas - edematous tissue surrounding mutually-exclusive regions of enhancing and non-enhancing tumor. We trained multiple deep neural networks with a 3D U-Net arc"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1709.02967","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2017-09-09T15:57:51Z","cross_cats_sorted":[],"title_canon_sha256":"fe5a293206a45054a52492711a0291fb709305cfa492a2cd6d282b2e549705c8","abstract_canon_sha256":"5254d03c5cc7b1b19fc37c969bddc6efc5a06ac572aa74e8cfe6766e6d56b8ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:39.888848Z","signature_b64":"wTGvr5iQ5aLbzW7k2/9VnW2QOfW9701IzF4qM8ncLZdX3HT/p5ybq4YaZZ4Uka7UJUi45DPRtZspKNJTLRjnBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb34dec8e1ee71756e210f1dc16f8e516a45db13faa8ed364886d865c52f1700","last_reissued_at":"2026-05-18T00:35:39.888196Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:39.888196Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrew Beers, Bruce Rosen, CP Mammen, Elizabeth Gerstner, Emmett Sartor, James Brown, Jayashree Kalpathy-Cramer, Ken Chang","submitted_at":"2017-09-09T15:57:51Z","abstract_excerpt":"Deep learning has quickly become the weapon of choice for brain lesion segmentation. However, few existing algorithms pre-configure any biological context of their chosen segmentation tissues, and instead rely on the neural network's optimizer to develop such associations de novo. We present a novel method for applying deep neural networks to the problem of glioma tissue segmentation that takes into account the structured nature of gliomas - edematous tissue surrounding mutually-exclusive regions of enhancing and non-enhancing tumor. We trained multiple deep neural networks with a 3D U-Net arc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.02967","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1709.02967","created_at":"2026-05-18T00:35:39.888297+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.02967v1","created_at":"2026-05-18T00:35:39.888297+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.02967","created_at":"2026-05-18T00:35:39.888297+00:00"},{"alias_kind":"pith_short_12","alias_value":"XM2N5SHB5ZYX","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"XM2N5SHB5ZYXK3RB","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"XM2N5SHB","created_at":"2026-05-18T12:31:56.362134+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XM2N5SHB5ZYXK3RBB4O4C34OKF","json":"https://pith.science/pith/XM2N5SHB5ZYXK3RBB4O4C34OKF.json","graph_json":"https://pith.science/api/pith-number/XM2N5SHB5ZYXK3RBB4O4C34OKF/graph.json","events_json":"https://pith.science/api/pith-number/XM2N5SHB5ZYXK3RBB4O4C34OKF/events.json","paper":"https://pith.science/paper/XM2N5SHB"},"agent_actions":{"view_html":"https://pith.science/pith/XM2N5SHB5ZYXK3RBB4O4C34OKF","download_json":"https://pith.science/pith/XM2N5SHB5ZYXK3RBB4O4C34OKF.json","view_paper":"https://pith.science/paper/XM2N5SHB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.02967&json=true","fetch_graph":"https://pith.science/api/pith-number/XM2N5SHB5ZYXK3RBB4O4C34OKF/graph.json","fetch_events":"https://pith.science/api/pith-number/XM2N5SHB5ZYXK3RBB4O4C34OKF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XM2N5SHB5ZYXK3RBB4O4C34OKF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XM2N5SHB5ZYXK3RBB4O4C34OKF/action/storage_attestation","attest_author":"https://pith.science/pith/XM2N5SHB5ZYXK3RBB4O4C34OKF/action/author_attestation","sign_citation":"https://pith.science/pith/XM2N5SHB5ZYXK3RBB4O4C34OKF/action/citation_signature","submit_replication":"https://pith.science/pith/XM2N5SHB5ZYXK3RBB4O4C34OKF/action/replication_record"}},"created_at":"2026-05-18T00:35:39.888297+00:00","updated_at":"2026-05-18T00:35:39.888297+00:00"}