{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:QGTALFQWBHV3NGC34IFCC6OSGR","short_pith_number":"pith:QGTALFQW","schema_version":"1.0","canonical_sha256":"81a605961609ebb6985be20a2179d23469afed7aa19cd65459744f4cebbe23f5","source":{"kind":"arxiv","id":"1705.10545","version":1},"attestation_state":"computed","paper":{"title":"Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hannah Spitzer, Katrin Amunts, Stefan Harmeling, Timo Dickscheid","submitted_at":"2017-05-30T11:05:00Z","abstract_excerpt":"Microscopic analysis of histological sections is considered the \"gold standard\" to verify structural parcellations in the human brain. Its high resolution allows the study of laminar and columnar patterns of cell distributions, which build an important basis for the simulation of cortical areas and networks. However, such cytoarchitectonic mapping is a semiautomatic, time consuming process that does not scale with high throughput imaging. We present an automatic approach for parcellating histological sections at 2um resolution. It is based on a convolutional neural network that combines topolo"},"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":"1705.10545","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-05-30T11:05:00Z","cross_cats_sorted":[],"title_canon_sha256":"c6425b9b88dc611f6f866375400202af2895d294313633ea188a8244a4d0bfc3","abstract_canon_sha256":"d04a4ab83f7934118d4b6af30bb6cf04f0bf8cf4c0d4f0cf7f28f311f6b596e4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:12.125492Z","signature_b64":"VUTOJ3bve5/6oMRbOBib2s5CtCQitt9ciPq/XOOTEpAhb8SD7v9CspVAUGeO8l38qSRXT67CAeiplUGcdmEzCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81a605961609ebb6985be20a2179d23469afed7aa19cd65459744f4cebbe23f5","last_reissued_at":"2026-05-18T00:39:12.124841Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:12.124841Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hannah Spitzer, Katrin Amunts, Stefan Harmeling, Timo Dickscheid","submitted_at":"2017-05-30T11:05:00Z","abstract_excerpt":"Microscopic analysis of histological sections is considered the \"gold standard\" to verify structural parcellations in the human brain. Its high resolution allows the study of laminar and columnar patterns of cell distributions, which build an important basis for the simulation of cortical areas and networks. However, such cytoarchitectonic mapping is a semiautomatic, time consuming process that does not scale with high throughput imaging. We present an automatic approach for parcellating histological sections at 2um resolution. It is based on a convolutional neural network that combines topolo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.10545","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":"1705.10545","created_at":"2026-05-18T00:39:12.124944+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.10545v1","created_at":"2026-05-18T00:39:12.124944+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.10545","created_at":"2026-05-18T00:39:12.124944+00:00"},{"alias_kind":"pith_short_12","alias_value":"QGTALFQWBHV3","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_16","alias_value":"QGTALFQWBHV3NGC3","created_at":"2026-05-18T12:31:37.085036+00:00"},{"alias_kind":"pith_short_8","alias_value":"QGTALFQW","created_at":"2026-05-18T12:31:37.085036+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/QGTALFQWBHV3NGC34IFCC6OSGR","json":"https://pith.science/pith/QGTALFQWBHV3NGC34IFCC6OSGR.json","graph_json":"https://pith.science/api/pith-number/QGTALFQWBHV3NGC34IFCC6OSGR/graph.json","events_json":"https://pith.science/api/pith-number/QGTALFQWBHV3NGC34IFCC6OSGR/events.json","paper":"https://pith.science/paper/QGTALFQW"},"agent_actions":{"view_html":"https://pith.science/pith/QGTALFQWBHV3NGC34IFCC6OSGR","download_json":"https://pith.science/pith/QGTALFQWBHV3NGC34IFCC6OSGR.json","view_paper":"https://pith.science/paper/QGTALFQW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.10545&json=true","fetch_graph":"https://pith.science/api/pith-number/QGTALFQWBHV3NGC34IFCC6OSGR/graph.json","fetch_events":"https://pith.science/api/pith-number/QGTALFQWBHV3NGC34IFCC6OSGR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QGTALFQWBHV3NGC34IFCC6OSGR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QGTALFQWBHV3NGC34IFCC6OSGR/action/storage_attestation","attest_author":"https://pith.science/pith/QGTALFQWBHV3NGC34IFCC6OSGR/action/author_attestation","sign_citation":"https://pith.science/pith/QGTALFQWBHV3NGC34IFCC6OSGR/action/citation_signature","submit_replication":"https://pith.science/pith/QGTALFQWBHV3NGC34IFCC6OSGR/action/replication_record"}},"created_at":"2026-05-18T00:39:12.124944+00:00","updated_at":"2026-05-18T00:39:12.124944+00:00"}