{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:V6H74QMHETTB77NTKMYI6R4Z3V","short_pith_number":"pith:V6H74QMH","schema_version":"1.0","canonical_sha256":"af8ffe418724e61ffdb353308f4799dd7d788832e2c0c316d0e28f5f2bb1dbfd","source":{"kind":"arxiv","id":"2506.23721","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning-Based Semantic Segmentation for Real-Time Kidney Imaging and Measurements with Augmented Reality-Assisted Ultrasound","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.HC","cs.LG"],"primary_cat":"eess.IV","authors_text":"Domenico Buongiorno, Gijs Luijten, Jan Egger, Jens Kleesiek, Lisle Faray de Paiva, Peter Hoyer, Roberto Maria Scardigno, Vitoantonio Bevilacqua","submitted_at":"2025-06-30T10:49:54Z","abstract_excerpt":"Ultrasound (US) is widely accessible and radiation-free but has a steep learning curve due to its dynamic nature and non-standard imaging planes. Additionally, the constant need to shift focus between the US screen and the patient poses a challenge. To address these issues, we integrate deep learning (DL)-based semantic segmentation for real-time (RT) automated kidney volumetric measurements, which are essential for clinical assessment but are traditionally time-consuming and prone to fatigue. This automation allows clinicians to concentrate on image interpretation rather than manual measureme"},"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":"2506.23721","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2025-06-30T10:49:54Z","cross_cats_sorted":["cs.AI","cs.CV","cs.HC","cs.LG"],"title_canon_sha256":"9a1a0e8848dcf380f4611d6dbe13bcd73293a1f391ca0f032ce6fe8a8a99079a","abstract_canon_sha256":"6e7944fe1b863ed3cb4d7f3a90c3d47f1316535e55e79291407dbc7e2cb110bc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:29:27.069115Z","signature_b64":"RhQRsGzp5bC4CugsxHOPFzpB8AlD3ljbLzkHgytyk7T8MHbT545t30/Caq3zBYRZ75cW1JnyzXC+w20/XsvqDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af8ffe418724e61ffdb353308f4799dd7d788832e2c0c316d0e28f5f2bb1dbfd","last_reissued_at":"2026-07-05T11:29:27.068478Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:29:27.068478Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning-Based Semantic Segmentation for Real-Time Kidney Imaging and Measurements with Augmented Reality-Assisted Ultrasound","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.HC","cs.LG"],"primary_cat":"eess.IV","authors_text":"Domenico Buongiorno, Gijs Luijten, Jan Egger, Jens Kleesiek, Lisle Faray de Paiva, Peter Hoyer, Roberto Maria Scardigno, Vitoantonio Bevilacqua","submitted_at":"2025-06-30T10:49:54Z","abstract_excerpt":"Ultrasound (US) is widely accessible and radiation-free but has a steep learning curve due to its dynamic nature and non-standard imaging planes. Additionally, the constant need to shift focus between the US screen and the patient poses a challenge. To address these issues, we integrate deep learning (DL)-based semantic segmentation for real-time (RT) automated kidney volumetric measurements, which are essential for clinical assessment but are traditionally time-consuming and prone to fatigue. This automation allows clinicians to concentrate on image interpretation rather than manual measureme"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.23721","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.23721/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2506.23721","created_at":"2026-07-05T11:29:27.068559+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.23721v1","created_at":"2026-07-05T11:29:27.068559+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.23721","created_at":"2026-07-05T11:29:27.068559+00:00"},{"alias_kind":"pith_short_12","alias_value":"V6H74QMHETTB","created_at":"2026-07-05T11:29:27.068559+00:00"},{"alias_kind":"pith_short_16","alias_value":"V6H74QMHETTB77NT","created_at":"2026-07-05T11:29:27.068559+00:00"},{"alias_kind":"pith_short_8","alias_value":"V6H74QMH","created_at":"2026-07-05T11:29:27.068559+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/V6H74QMHETTB77NTKMYI6R4Z3V","json":"https://pith.science/pith/V6H74QMHETTB77NTKMYI6R4Z3V.json","graph_json":"https://pith.science/api/pith-number/V6H74QMHETTB77NTKMYI6R4Z3V/graph.json","events_json":"https://pith.science/api/pith-number/V6H74QMHETTB77NTKMYI6R4Z3V/events.json","paper":"https://pith.science/paper/V6H74QMH"},"agent_actions":{"view_html":"https://pith.science/pith/V6H74QMHETTB77NTKMYI6R4Z3V","download_json":"https://pith.science/pith/V6H74QMHETTB77NTKMYI6R4Z3V.json","view_paper":"https://pith.science/paper/V6H74QMH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.23721&json=true","fetch_graph":"https://pith.science/api/pith-number/V6H74QMHETTB77NTKMYI6R4Z3V/graph.json","fetch_events":"https://pith.science/api/pith-number/V6H74QMHETTB77NTKMYI6R4Z3V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V6H74QMHETTB77NTKMYI6R4Z3V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V6H74QMHETTB77NTKMYI6R4Z3V/action/storage_attestation","attest_author":"https://pith.science/pith/V6H74QMHETTB77NTKMYI6R4Z3V/action/author_attestation","sign_citation":"https://pith.science/pith/V6H74QMHETTB77NTKMYI6R4Z3V/action/citation_signature","submit_replication":"https://pith.science/pith/V6H74QMHETTB77NTKMYI6R4Z3V/action/replication_record"}},"created_at":"2026-07-05T11:29:27.068559+00:00","updated_at":"2026-07-05T11:29:27.068559+00:00"}