{"id":"52889d82-5094-4b25-8ae3-37d6f35c586c","arxiv_id":"2505.14017","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":7.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"A domain-randomized neural network reconstructs cortical surfaces from heterogeneous clinical MRI scans, cutting cortical thickness error by roughly half versus recon-all-clinical.","lead":"The paper trains a neural network, using only synthetic MRI data, to trace the folded inner and outer surfaces of the brain cortex directly from messy clinical scans of varied contrast and resolution. It reports about half the cortical thickness error of the previous best clinical method, recon-all-clinical, and runs in about one second on a GPU.","discovery_kind":"new_method","skeptic_critique":null,"referee_report":null,"author_rebuttal":null,"desk_editor":null,"rs_alignment":null,"lean_confirmation":null,"pith_extraction":null,"created_at":"2026-08-07T15:42:36.332199+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":null,"supporting_citations":[],"review_version":1}