{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ZXV5IGOSWGALYUSGN3EX44XYW4","short_pith_number":"pith:ZXV5IGOS","schema_version":"1.0","canonical_sha256":"cdebd419d2b180bc52466ec97e72f8b709474345f9a69ce514f8ddbd54b0923f","source":{"kind":"arxiv","id":"1902.01977","version":1},"attestation_state":"computed","paper":{"title":"Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Akshay S. Chaudhari, Arjun D. Desai, Brian A. Hargreaves, Garry E. Gold","submitted_at":"2019-02-05T23:39:17Z","abstract_excerpt":"High-fidelity semantic segmentation of magnetic resonance volumes is critical for estimating tissue morphometry and relaxation parameters in both clinical and research applications. While manual segmentation is accepted as the gold-standard, recent advances in deep learning and convolutional neural networks (CNNs) have shown promise for efficient automatic segmentation of soft tissues. However, due to the stochastic nature of deep learning and the multitude of hyperparameters in training networks, predicting network behavior is challenging. In this paper, we quantify the impact of three factor"},"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":"1902.01977","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-02-05T23:39:17Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"857af6f7a10105c6bf2490fbfe1c6a24e77528d2aa06f7964abc2ce90215196e","abstract_canon_sha256":"bf9b98b98bce04607939b074b79b56b472df0a4c48a2b0d31780d883845047c5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:38.189331Z","signature_b64":"2O7qDpWUuMVXessdzDDKmCtxgcv9T97+SZ6tG10DAxyN5Tsttd38cahexGoju5t/S1Rf3+JKbByVsuFQgLehBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cdebd419d2b180bc52466ec97e72f8b709474345f9a69ce514f8ddbd54b0923f","last_reissued_at":"2026-05-17T23:54:38.188822Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:38.188822Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Akshay S. Chaudhari, Arjun D. Desai, Brian A. Hargreaves, Garry E. Gold","submitted_at":"2019-02-05T23:39:17Z","abstract_excerpt":"High-fidelity semantic segmentation of magnetic resonance volumes is critical for estimating tissue morphometry and relaxation parameters in both clinical and research applications. While manual segmentation is accepted as the gold-standard, recent advances in deep learning and convolutional neural networks (CNNs) have shown promise for efficient automatic segmentation of soft tissues. However, due to the stochastic nature of deep learning and the multitude of hyperparameters in training networks, predicting network behavior is challenging. In this paper, we quantify the impact of three factor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.01977","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":"1902.01977","created_at":"2026-05-17T23:54:38.188921+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.01977v1","created_at":"2026-05-17T23:54:38.188921+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.01977","created_at":"2026-05-17T23:54:38.188921+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZXV5IGOSWGAL","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZXV5IGOSWGALYUSG","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZXV5IGOS","created_at":"2026-05-18T12:33:33.725879+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/ZXV5IGOSWGALYUSGN3EX44XYW4","json":"https://pith.science/pith/ZXV5IGOSWGALYUSGN3EX44XYW4.json","graph_json":"https://pith.science/api/pith-number/ZXV5IGOSWGALYUSGN3EX44XYW4/graph.json","events_json":"https://pith.science/api/pith-number/ZXV5IGOSWGALYUSGN3EX44XYW4/events.json","paper":"https://pith.science/paper/ZXV5IGOS"},"agent_actions":{"view_html":"https://pith.science/pith/ZXV5IGOSWGALYUSGN3EX44XYW4","download_json":"https://pith.science/pith/ZXV5IGOSWGALYUSGN3EX44XYW4.json","view_paper":"https://pith.science/paper/ZXV5IGOS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.01977&json=true","fetch_graph":"https://pith.science/api/pith-number/ZXV5IGOSWGALYUSGN3EX44XYW4/graph.json","fetch_events":"https://pith.science/api/pith-number/ZXV5IGOSWGALYUSGN3EX44XYW4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZXV5IGOSWGALYUSGN3EX44XYW4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZXV5IGOSWGALYUSGN3EX44XYW4/action/storage_attestation","attest_author":"https://pith.science/pith/ZXV5IGOSWGALYUSGN3EX44XYW4/action/author_attestation","sign_citation":"https://pith.science/pith/ZXV5IGOSWGALYUSGN3EX44XYW4/action/citation_signature","submit_replication":"https://pith.science/pith/ZXV5IGOSWGALYUSGN3EX44XYW4/action/replication_record"}},"created_at":"2026-05-17T23:54:38.188921+00:00","updated_at":"2026-05-17T23:54:38.188921+00:00"}