{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:W6EXUM6BLJ4J5DTXI7LXOY4IJC","short_pith_number":"pith:W6EXUM6B","schema_version":"1.0","canonical_sha256":"b7897a33c15a789e8e7747d77763884896f0764006dba1de1e8e64dd26dcceb3","source":{"kind":"arxiv","id":"2312.11580","version":2},"attestation_state":"computed","paper":{"title":"PlaNet-S: Automatic Semantic Segmentation of Placenta","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Ayaka Harigai, Eichi Takaya, Isso Saito, Kei Takase, Shinnosuke Yamamoto, Takuya Ueda, Tomomi Sato, Tomoya Kobayashi","submitted_at":"2023-12-18T10:55:11Z","abstract_excerpt":"[Purpose] To develop a fully automated semantic placenta segmentation model that integrates the U-Net and SegNeXt architectures through ensemble learning. [Methods] A total of 218 pregnant women with suspected placental anomalies who underwent magnetic resonance imaging (MRI) were enrolled, yielding 1090 annotated images for developing a deep learning model for placental segmentation. The images were standardized and divided into training and test sets. The performance of PlaNet-S, which integrates U-Net and SegNeXt within an ensemble framework, was assessed using Intersection over Union (IoU)"},"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":"2312.11580","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2023-12-18T10:55:11Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"b57deaa1b25ce41e0aaf0da7ff60037462e7f5d9a858d3f78c8f3d2b4b42f916","abstract_canon_sha256":"83be82afa8082bd97d493f529c5a467261f4605f859a29662988c3583dbd137b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:36.787300Z","signature_b64":"yLK+trrp4iJw5exX/liZFvbRMirAjA4BwLhbKLrE/TufKXwDU062WXPiaazZSbCOAIE9qaEO8sSnYXCnw5vWCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7897a33c15a789e8e7747d77763884896f0764006dba1de1e8e64dd26dcceb3","last_reissued_at":"2026-07-05T11:15:36.786751Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:36.786751Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PlaNet-S: Automatic Semantic Segmentation of Placenta","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Ayaka Harigai, Eichi Takaya, Isso Saito, Kei Takase, Shinnosuke Yamamoto, Takuya Ueda, Tomomi Sato, Tomoya Kobayashi","submitted_at":"2023-12-18T10:55:11Z","abstract_excerpt":"[Purpose] To develop a fully automated semantic placenta segmentation model that integrates the U-Net and SegNeXt architectures through ensemble learning. [Methods] A total of 218 pregnant women with suspected placental anomalies who underwent magnetic resonance imaging (MRI) were enrolled, yielding 1090 annotated images for developing a deep learning model for placental segmentation. The images were standardized and divided into training and test sets. The performance of PlaNet-S, which integrates U-Net and SegNeXt within an ensemble framework, was assessed using Intersection over Union (IoU)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.11580","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2312.11580/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":"2312.11580","created_at":"2026-07-05T11:15:36.786816+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.11580v2","created_at":"2026-07-05T11:15:36.786816+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.11580","created_at":"2026-07-05T11:15:36.786816+00:00"},{"alias_kind":"pith_short_12","alias_value":"W6EXUM6BLJ4J","created_at":"2026-07-05T11:15:36.786816+00:00"},{"alias_kind":"pith_short_16","alias_value":"W6EXUM6BLJ4J5DTX","created_at":"2026-07-05T11:15:36.786816+00:00"},{"alias_kind":"pith_short_8","alias_value":"W6EXUM6B","created_at":"2026-07-05T11:15:36.786816+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.24739","citing_title":"Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W6EXUM6BLJ4J5DTXI7LXOY4IJC","json":"https://pith.science/pith/W6EXUM6BLJ4J5DTXI7LXOY4IJC.json","graph_json":"https://pith.science/api/pith-number/W6EXUM6BLJ4J5DTXI7LXOY4IJC/graph.json","events_json":"https://pith.science/api/pith-number/W6EXUM6BLJ4J5DTXI7LXOY4IJC/events.json","paper":"https://pith.science/paper/W6EXUM6B"},"agent_actions":{"view_html":"https://pith.science/pith/W6EXUM6BLJ4J5DTXI7LXOY4IJC","download_json":"https://pith.science/pith/W6EXUM6BLJ4J5DTXI7LXOY4IJC.json","view_paper":"https://pith.science/paper/W6EXUM6B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.11580&json=true","fetch_graph":"https://pith.science/api/pith-number/W6EXUM6BLJ4J5DTXI7LXOY4IJC/graph.json","fetch_events":"https://pith.science/api/pith-number/W6EXUM6BLJ4J5DTXI7LXOY4IJC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W6EXUM6BLJ4J5DTXI7LXOY4IJC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W6EXUM6BLJ4J5DTXI7LXOY4IJC/action/storage_attestation","attest_author":"https://pith.science/pith/W6EXUM6BLJ4J5DTXI7LXOY4IJC/action/author_attestation","sign_citation":"https://pith.science/pith/W6EXUM6BLJ4J5DTXI7LXOY4IJC/action/citation_signature","submit_replication":"https://pith.science/pith/W6EXUM6BLJ4J5DTXI7LXOY4IJC/action/replication_record"}},"created_at":"2026-07-05T11:15:36.786816+00:00","updated_at":"2026-07-05T11:15:36.786816+00:00"}