{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:HYIVGFENCT7OELBU4TTWVCE7TA","short_pith_number":"pith:HYIVGFEN","schema_version":"1.0","canonical_sha256":"3e1153148d14fee22c34e4e76a889f980cf4c804eef858046e9255f2a71c5132","source":{"kind":"arxiv","id":"1505.06236","version":2},"attestation_state":"computed","paper":{"title":"A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Amal Farag, Evrim Turkbey, Holger R. Roth, Jiamin Liu, Le Lu, Ronald M. Summers","submitted_at":"2015-05-22T21:59:45Z","abstract_excerpt":"Robust automated organ segmentation is a prerequisite for computer-aided diagnosis (CAD), quantitative imaging analysis and surgical assistance. For high-variability organs such as the pancreas, previous approaches report undesirably low accuracies. We present a bottom-up approach for pancreas segmentation in abdominal CT scans that is based on a hierarchy of information propagation by classifying image patches at different resolutions; and cascading superpixels. There are four stages: 1) decomposing CT slice images as a set of disjoint boundary-preserving superpixels; 2) computing pancreas cl"},"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":"1505.06236","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2015-05-22T21:59:45Z","cross_cats_sorted":[],"title_canon_sha256":"7df2d8303fe58ec683edb331b0ea59bc330bf96cbb9350e820dec6dee6811c8f","abstract_canon_sha256":"20f0145e59dda6951dcfe1aeb3d28068f0b4292bd503ade75351b83ef7c65aa4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:19:34.343898Z","signature_b64":"pX9ppGUnLzD1r1P9m74y+St8TjRbSEcnuSMZ36hXLGpJ8CZwlyoVDoWe9vKAAMH9q4TCePkUZXn6W5qKroGjAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3e1153148d14fee22c34e4e76a889f980cf4c804eef858046e9255f2a71c5132","last_reissued_at":"2026-05-18T01:19:34.343369Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:19:34.343369Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Amal Farag, Evrim Turkbey, Holger R. Roth, Jiamin Liu, Le Lu, Ronald M. Summers","submitted_at":"2015-05-22T21:59:45Z","abstract_excerpt":"Robust automated organ segmentation is a prerequisite for computer-aided diagnosis (CAD), quantitative imaging analysis and surgical assistance. For high-variability organs such as the pancreas, previous approaches report undesirably low accuracies. We present a bottom-up approach for pancreas segmentation in abdominal CT scans that is based on a hierarchy of information propagation by classifying image patches at different resolutions; and cascading superpixels. There are four stages: 1) decomposing CT slice images as a set of disjoint boundary-preserving superpixels; 2) computing pancreas cl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1505.06236","kind":"arxiv","version":2},"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":"1505.06236","created_at":"2026-05-18T01:19:34.343469+00:00"},{"alias_kind":"arxiv_version","alias_value":"1505.06236v2","created_at":"2026-05-18T01:19:34.343469+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1505.06236","created_at":"2026-05-18T01:19:34.343469+00:00"},{"alias_kind":"pith_short_12","alias_value":"HYIVGFENCT7O","created_at":"2026-05-18T12:29:25.134429+00:00"},{"alias_kind":"pith_short_16","alias_value":"HYIVGFENCT7OELBU","created_at":"2026-05-18T12:29:25.134429+00:00"},{"alias_kind":"pith_short_8","alias_value":"HYIVGFEN","created_at":"2026-05-18T12:29:25.134429+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/HYIVGFENCT7OELBU4TTWVCE7TA","json":"https://pith.science/pith/HYIVGFENCT7OELBU4TTWVCE7TA.json","graph_json":"https://pith.science/api/pith-number/HYIVGFENCT7OELBU4TTWVCE7TA/graph.json","events_json":"https://pith.science/api/pith-number/HYIVGFENCT7OELBU4TTWVCE7TA/events.json","paper":"https://pith.science/paper/HYIVGFEN"},"agent_actions":{"view_html":"https://pith.science/pith/HYIVGFENCT7OELBU4TTWVCE7TA","download_json":"https://pith.science/pith/HYIVGFENCT7OELBU4TTWVCE7TA.json","view_paper":"https://pith.science/paper/HYIVGFEN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1505.06236&json=true","fetch_graph":"https://pith.science/api/pith-number/HYIVGFENCT7OELBU4TTWVCE7TA/graph.json","fetch_events":"https://pith.science/api/pith-number/HYIVGFENCT7OELBU4TTWVCE7TA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HYIVGFENCT7OELBU4TTWVCE7TA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HYIVGFENCT7OELBU4TTWVCE7TA/action/storage_attestation","attest_author":"https://pith.science/pith/HYIVGFENCT7OELBU4TTWVCE7TA/action/author_attestation","sign_citation":"https://pith.science/pith/HYIVGFENCT7OELBU4TTWVCE7TA/action/citation_signature","submit_replication":"https://pith.science/pith/HYIVGFENCT7OELBU4TTWVCE7TA/action/replication_record"}},"created_at":"2026-05-18T01:19:34.343469+00:00","updated_at":"2026-05-18T01:19:34.343469+00:00"}