{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:2RG6KWSNG3T65Z7FXRL5E37TTT","short_pith_number":"pith:2RG6KWSN","schema_version":"1.0","canonical_sha256":"d44de55a4d36e7eee7e5bc57d26ff39cf2c7a41b99d011b19dd1a1d408178eb0","source":{"kind":"arxiv","id":"1806.06769","version":1},"attestation_state":"computed","paper":{"title":"Kid-Net: Convolution Networks for Kidney Vessels Segmentation from CT-Volumes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ahmed Taha, Junning Li, Pechin Lo, Tao Zhao","submitted_at":"2018-06-18T15:25:07Z","abstract_excerpt":"Semantic image segmentation plays an important role in modeling patient-specific anatomy. We propose a convolution neural network, called Kid-Net, along with a training schema to segment kidney vessels: artery, vein and collecting system. Such segmentation is vital during the surgical planning phase in which medical decisions are made before surgical incision. Our main contribution is developing a training schema that handles unbalanced data, reduces false positives and enables high-resolution segmentation with a limited memory budget. These objectives are attained using dynamic weighting, ran"},"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":"1806.06769","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-18T15:25:07Z","cross_cats_sorted":[],"title_canon_sha256":"30247b76e2420b1a603d2f0bf445c8b7eb98e5c03e592f123a346f91591dd8a7","abstract_canon_sha256":"e11436bd43f528db9922d3dffc13dd940b9f69ee8ca2b581e16b3c675dce9198"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:59.148214Z","signature_b64":"Vmajsto8UIWDfTkE82c2f00vkkxQqrLnX4wihZwacclsnHYsqUNHje4PKJ7aH6FLq0LzD9mrElv/hPXZsKkWDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d44de55a4d36e7eee7e5bc57d26ff39cf2c7a41b99d011b19dd1a1d408178eb0","last_reissued_at":"2026-05-18T00:12:59.147464Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:59.147464Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Kid-Net: Convolution Networks for Kidney Vessels Segmentation from CT-Volumes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ahmed Taha, Junning Li, Pechin Lo, Tao Zhao","submitted_at":"2018-06-18T15:25:07Z","abstract_excerpt":"Semantic image segmentation plays an important role in modeling patient-specific anatomy. We propose a convolution neural network, called Kid-Net, along with a training schema to segment kidney vessels: artery, vein and collecting system. Such segmentation is vital during the surgical planning phase in which medical decisions are made before surgical incision. Our main contribution is developing a training schema that handles unbalanced data, reduces false positives and enables high-resolution segmentation with a limited memory budget. These objectives are attained using dynamic weighting, ran"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.06769","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":"1806.06769","created_at":"2026-05-18T00:12:59.147587+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.06769v1","created_at":"2026-05-18T00:12:59.147587+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.06769","created_at":"2026-05-18T00:12:59.147587+00:00"},{"alias_kind":"pith_short_12","alias_value":"2RG6KWSNG3T6","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"2RG6KWSNG3T65Z7F","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"2RG6KWSN","created_at":"2026-05-18T12:32:02.567920+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/2RG6KWSNG3T65Z7FXRL5E37TTT","json":"https://pith.science/pith/2RG6KWSNG3T65Z7FXRL5E37TTT.json","graph_json":"https://pith.science/api/pith-number/2RG6KWSNG3T65Z7FXRL5E37TTT/graph.json","events_json":"https://pith.science/api/pith-number/2RG6KWSNG3T65Z7FXRL5E37TTT/events.json","paper":"https://pith.science/paper/2RG6KWSN"},"agent_actions":{"view_html":"https://pith.science/pith/2RG6KWSNG3T65Z7FXRL5E37TTT","download_json":"https://pith.science/pith/2RG6KWSNG3T65Z7FXRL5E37TTT.json","view_paper":"https://pith.science/paper/2RG6KWSN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.06769&json=true","fetch_graph":"https://pith.science/api/pith-number/2RG6KWSNG3T65Z7FXRL5E37TTT/graph.json","fetch_events":"https://pith.science/api/pith-number/2RG6KWSNG3T65Z7FXRL5E37TTT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2RG6KWSNG3T65Z7FXRL5E37TTT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2RG6KWSNG3T65Z7FXRL5E37TTT/action/storage_attestation","attest_author":"https://pith.science/pith/2RG6KWSNG3T65Z7FXRL5E37TTT/action/author_attestation","sign_citation":"https://pith.science/pith/2RG6KWSNG3T65Z7FXRL5E37TTT/action/citation_signature","submit_replication":"https://pith.science/pith/2RG6KWSNG3T65Z7FXRL5E37TTT/action/replication_record"}},"created_at":"2026-05-18T00:12:59.147587+00:00","updated_at":"2026-05-18T00:12:59.147587+00:00"}