{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:EEC5QKKMQX2RIZ7TDCGJ3TSTDZ","short_pith_number":"pith:EEC5QKKM","schema_version":"1.0","canonical_sha256":"2105d8294c85f51467f3188c9dce531e5cbe6e9d22147c5be67728b7cc346388","source":{"kind":"arxiv","id":"2001.05720","version":1},"attestation_state":"computed","paper":{"title":"Deep ICE: A Deep learning approach for MRI Intracranial Cavity Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"q-bio.QM","authors_text":"Fernando Aparici-Robles, Gregorio Rubio-Navarro, Jose E. Romero, Jos\\'e V. Manj\\'on, Mar\\'ia De la Iglesia-Vaya, Pierrick Coup\\'e, Roberto Vivo-Hernando","submitted_at":"2020-01-16T10:00:26Z","abstract_excerpt":"Automatic methods for measuring normalized regional brain volumes from MRI data are a key tool to help in the objective diagnostic and follow-up of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume is commonly used for normalization. In this paper, we present an accurate and efficient approach to automatically segment the intracranial cavity using a volumetric 3D convolutional neural network and a new 3D patch extraction strategy specially adapted to deal with the traditional low number of training cases available in supervised segmentation and"},"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":"2001.05720","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2020-01-16T10:00:26Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"fbf8b28dcba855098d6ccc82644507330ea09e6d0dd811d2645dd57da2f40e4c","abstract_canon_sha256":"0f740961bb389f2f84998590ad24a11b24fdece8758f5a069f18e5d0af19e2ef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:33:55.228677Z","signature_b64":"d7+SjIlUkr4UpiTmnnwc/JuJ5+SDJpv6k0+UfERpSbm2SWOHhnH+NywzKsdEGEodw78r23ziDP5fPTUGq36ACA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2105d8294c85f51467f3188c9dce531e5cbe6e9d22147c5be67728b7cc346388","last_reissued_at":"2026-07-05T00:33:55.228238Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:33:55.228238Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep ICE: A Deep learning approach for MRI Intracranial Cavity Extraction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"q-bio.QM","authors_text":"Fernando Aparici-Robles, Gregorio Rubio-Navarro, Jose E. Romero, Jos\\'e V. Manj\\'on, Mar\\'ia De la Iglesia-Vaya, Pierrick Coup\\'e, Roberto Vivo-Hernando","submitted_at":"2020-01-16T10:00:26Z","abstract_excerpt":"Automatic methods for measuring normalized regional brain volumes from MRI data are a key tool to help in the objective diagnostic and follow-up of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume is commonly used for normalization. In this paper, we present an accurate and efficient approach to automatically segment the intracranial cavity using a volumetric 3D convolutional neural network and a new 3D patch extraction strategy specially adapted to deal with the traditional low number of training cases available in supervised segmentation and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.05720","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2001.05720/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2001.05720","created_at":"2026-07-05T00:33:55.228302+00:00"},{"alias_kind":"arxiv_version","alias_value":"2001.05720v1","created_at":"2026-07-05T00:33:55.228302+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.05720","created_at":"2026-07-05T00:33:55.228302+00:00"},{"alias_kind":"pith_short_12","alias_value":"EEC5QKKMQX2R","created_at":"2026-07-05T00:33:55.228302+00:00"},{"alias_kind":"pith_short_16","alias_value":"EEC5QKKMQX2RIZ7T","created_at":"2026-07-05T00:33:55.228302+00:00"},{"alias_kind":"pith_short_8","alias_value":"EEC5QKKM","created_at":"2026-07-05T00:33:55.228302+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.03217","citing_title":"petBrain: A New Pipeline for Amyloid, Tau Tangles and Neurodegeneration Quantification Using PET and MRI","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EEC5QKKMQX2RIZ7TDCGJ3TSTDZ","json":"https://pith.science/pith/EEC5QKKMQX2RIZ7TDCGJ3TSTDZ.json","graph_json":"https://pith.science/api/pith-number/EEC5QKKMQX2RIZ7TDCGJ3TSTDZ/graph.json","events_json":"https://pith.science/api/pith-number/EEC5QKKMQX2RIZ7TDCGJ3TSTDZ/events.json","paper":"https://pith.science/paper/EEC5QKKM"},"agent_actions":{"view_html":"https://pith.science/pith/EEC5QKKMQX2RIZ7TDCGJ3TSTDZ","download_json":"https://pith.science/pith/EEC5QKKMQX2RIZ7TDCGJ3TSTDZ.json","view_paper":"https://pith.science/paper/EEC5QKKM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2001.05720&json=true","fetch_graph":"https://pith.science/api/pith-number/EEC5QKKMQX2RIZ7TDCGJ3TSTDZ/graph.json","fetch_events":"https://pith.science/api/pith-number/EEC5QKKMQX2RIZ7TDCGJ3TSTDZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EEC5QKKMQX2RIZ7TDCGJ3TSTDZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EEC5QKKMQX2RIZ7TDCGJ3TSTDZ/action/storage_attestation","attest_author":"https://pith.science/pith/EEC5QKKMQX2RIZ7TDCGJ3TSTDZ/action/author_attestation","sign_citation":"https://pith.science/pith/EEC5QKKMQX2RIZ7TDCGJ3TSTDZ/action/citation_signature","submit_replication":"https://pith.science/pith/EEC5QKKMQX2RIZ7TDCGJ3TSTDZ/action/replication_record"}},"created_at":"2026-07-05T00:33:55.228302+00:00","updated_at":"2026-07-05T00:33:55.228302+00:00"}