{"paper":{"title":"Dual channel CW nnU-Net for 3D PET-CT Lesion Segmentation in 2024 autoPET III Challenge","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Ching-Wei Wang, Keng-Wei Liu, Ting-Sheng Su","submitted_at":"2024-09-11T09:45:15Z","abstract_excerpt":"PET/CT is extensively used in imaging malignant tumors because it highlights areas of increased glucose metabolism, indicative of cancerous activity. Accurate 3D lesion segmentation in PET/CT imaging is essential for effective oncological diagnostics and treatment planning. In this study, we developed an advanced 3D residual U-Net model for the Automated Lesion Segmentation in Whole-Body PET/CT - Multitracer Multicenter Generalization (autoPET III) Challenge, which will be held jointly with 2024 Medical Image Computing and Computer Assisted Intervention (MICCAI) conference at Marrakesh, Morocc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.07144","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/2409.07144/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"}