{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:6HVBAYXIS5NRSKL7TAOYL3EAM2","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":"1fd7da456fee9b61eb8801804d66ac2447a9180d80de3fe0024fe25724f9bb0f","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-04-12T15:14:41Z","title_canon_sha256":"ae112ff428f0d4d649dc26fa47152ed8962518f2c96656f6accfdbaca720f7e8"},"schema_version":"1.0","source":{"id":"2304.05901","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.05901","created_at":"2026-07-05T06:00:25Z"},{"alias_kind":"arxiv_version","alias_value":"2304.05901v1","created_at":"2026-07-05T06:00:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.05901","created_at":"2026-07-05T06:00:25Z"},{"alias_kind":"pith_short_12","alias_value":"6HVBAYXIS5NR","created_at":"2026-07-05T06:00:25Z"},{"alias_kind":"pith_short_16","alias_value":"6HVBAYXIS5NRSKL7","created_at":"2026-07-05T06:00:25Z"},{"alias_kind":"pith_short_8","alias_value":"6HVBAYXI","created_at":"2026-07-05T06:00:25Z"}],"graph_snapshots":[{"event_id":"sha256:3f688c449743cb13dfdfadc187bc705f859161acaaae5a1354bdbfb979506ef7","target":"graph","created_at":"2026-07-05T06:00:25Z","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/2304.05901/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Medical image segmentation is an increasingly popular area of research in medical imaging processing and analysis. However, many researchers who are new to the field struggle with basic concepts. This tutorial paper aims to provide an overview of the fundamental concepts of medical imaging, with a focus on Magnetic Resonance and Computerized Tomography. We will also discuss deep learning algorithms, tools, and frameworks used for segmentation tasks, and suggest best practices for method development and image analysis. Our tutorial includes sample tasks using public data, and accompanying code ","authors_text":"Diedre Carmo, Gustavo Pinheiro, Let\\'icia Rittner, L\\'ivia Rodrigues, Roberto Lotufo, Thays Abreu","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-04-12T15:14:41Z","title":"Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.05901","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":"sha256:4c0bbeddea373a7f4edb85864c886e9d7acdadd48885e98d41db927cf9bbd056","target":"record","created_at":"2026-07-05T06:00:25Z","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":"1fd7da456fee9b61eb8801804d66ac2447a9180d80de3fe0024fe25724f9bb0f","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-04-12T15:14:41Z","title_canon_sha256":"ae112ff428f0d4d649dc26fa47152ed8962518f2c96656f6accfdbaca720f7e8"},"schema_version":"1.0","source":{"id":"2304.05901","kind":"arxiv","version":1}},"canonical_sha256":"f1ea1062e8975b19297f981d85ec8066937c83efd84a2785bd51ca0e5a0d5e2b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f1ea1062e8975b19297f981d85ec8066937c83efd84a2785bd51ca0e5a0d5e2b","first_computed_at":"2026-07-05T06:00:25.441805Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:00:25.441805Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XYd+uvCoNYzxdNNKXvQedw6P3Ocmqa7ewg08HsCC7b22z0sR/hR3qNWzc3NJP8NJ746kf7NPl8jKc92l1MjaCA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:00:25.442200Z","signed_message":"canonical_sha256_bytes"},"source_id":"2304.05901","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4c0bbeddea373a7f4edb85864c886e9d7acdadd48885e98d41db927cf9bbd056","sha256:3f688c449743cb13dfdfadc187bc705f859161acaaae5a1354bdbfb979506ef7"],"state_sha256":"3718e95d53b4a9692fb92a5960ccb68d7f3c08006399b79aa8ae49376aa928d9"}