{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:XRHHUT5YDCBIUL2BOWJ6SYSUAV","short_pith_number":"pith:XRHHUT5Y","schema_version":"1.0","canonical_sha256":"bc4e7a4fb818828a2f417593e96254054a056cebe5bd79ed2575e5567b722f52","source":{"kind":"arxiv","id":"1809.04282","version":1},"attestation_state":"computed","paper":{"title":"Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bhavna Antony, Dwarikanath Mahapatra, Rahil Garnavi, Suman Sedai","submitted_at":"2018-09-12T07:22:15Z","abstract_excerpt":"Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases. In this paper, we propose a method for retinal layer segmentation and quantification of uncertainty based on Bayesian deep learning. Our method not only performs end-to-end segmentation of retinal layers, but also gives the pixel wise uncertainty measure of the segmentation output. The generated uncertainty map can be used to identify erroneously segmented image regions which is useful in downstream analysis. We have validated our method on a dataset of 1487 images obtained from 15 "},"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":"1809.04282","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-12T07:22:15Z","cross_cats_sorted":[],"title_canon_sha256":"2752068f8e0cdb9ba520d761d34fa75c3b5b9db11bdc8eb34324b452f18c3a65","abstract_canon_sha256":"975670b9a9480f5c55685724a02ff20e061459f129eb14581c8548cf7d9c9d13"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:52.874327Z","signature_b64":"urPTdq+z9RlMFNbdEqIy59i4YqwZJ4A7CjIP/8QhtOesiOS77kvB6ig0hSW2nDUiw3e/xBYyPN6NuweDxsNXAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc4e7a4fb818828a2f417593e96254054a056cebe5bd79ed2575e5567b722f52","last_reissued_at":"2026-05-18T00:05:52.873750Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:52.873750Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bhavna Antony, Dwarikanath Mahapatra, Rahil Garnavi, Suman Sedai","submitted_at":"2018-09-12T07:22:15Z","abstract_excerpt":"Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases. In this paper, we propose a method for retinal layer segmentation and quantification of uncertainty based on Bayesian deep learning. Our method not only performs end-to-end segmentation of retinal layers, but also gives the pixel wise uncertainty measure of the segmentation output. The generated uncertainty map can be used to identify erroneously segmented image regions which is useful in downstream analysis. We have validated our method on a dataset of 1487 images obtained from 15 "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.04282","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":"1809.04282","created_at":"2026-05-18T00:05:52.873837+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.04282v1","created_at":"2026-05-18T00:05:52.873837+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.04282","created_at":"2026-05-18T00:05:52.873837+00:00"},{"alias_kind":"pith_short_12","alias_value":"XRHHUT5YDCBI","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_16","alias_value":"XRHHUT5YDCBIUL2B","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_8","alias_value":"XRHHUT5Y","created_at":"2026-05-18T12:33:01.666342+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/XRHHUT5YDCBIUL2BOWJ6SYSUAV","json":"https://pith.science/pith/XRHHUT5YDCBIUL2BOWJ6SYSUAV.json","graph_json":"https://pith.science/api/pith-number/XRHHUT5YDCBIUL2BOWJ6SYSUAV/graph.json","events_json":"https://pith.science/api/pith-number/XRHHUT5YDCBIUL2BOWJ6SYSUAV/events.json","paper":"https://pith.science/paper/XRHHUT5Y"},"agent_actions":{"view_html":"https://pith.science/pith/XRHHUT5YDCBIUL2BOWJ6SYSUAV","download_json":"https://pith.science/pith/XRHHUT5YDCBIUL2BOWJ6SYSUAV.json","view_paper":"https://pith.science/paper/XRHHUT5Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.04282&json=true","fetch_graph":"https://pith.science/api/pith-number/XRHHUT5YDCBIUL2BOWJ6SYSUAV/graph.json","fetch_events":"https://pith.science/api/pith-number/XRHHUT5YDCBIUL2BOWJ6SYSUAV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XRHHUT5YDCBIUL2BOWJ6SYSUAV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XRHHUT5YDCBIUL2BOWJ6SYSUAV/action/storage_attestation","attest_author":"https://pith.science/pith/XRHHUT5YDCBIUL2BOWJ6SYSUAV/action/author_attestation","sign_citation":"https://pith.science/pith/XRHHUT5YDCBIUL2BOWJ6SYSUAV/action/citation_signature","submit_replication":"https://pith.science/pith/XRHHUT5YDCBIUL2BOWJ6SYSUAV/action/replication_record"}},"created_at":"2026-05-18T00:05:52.873837+00:00","updated_at":"2026-05-18T00:05:52.873837+00:00"}