{"id":"W4388749536","doi":"10.1101/2023.11.16.23298572","title":"Fully automatic segmentation of brain lacunas resulting from resective surgery using a 3D deep learning model","year":2023,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Epilepsy research and treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada); University of Calgary","funders":"National Institutes of Health; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Segmentation; Artificial intelligence; Similarity (geometry); Deep learning; Correlation coefficient; Sørensen–Dice coefficient; Ground truth; Computer science; Quality (philosophy); Correlation; Pattern recognition (psychology); Machine learning; Image segmentation; Image (mathematics); Mathematics; Physics; Geometry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001070808,0.00109389,0.000791908,0.001531318,0.000305007,0.001422293,0.001034117,0.001262956,0.00128813],"category_scores_gemma":[0.002225199,0.0006838565,0.001366961,0.0007742321,0.0005120214,0.0007373639,0.001233358,0.0008800959,0.0006231964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009879397,"about_ca_system_score_gemma":0.001486725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008471617,"about_ca_topic_score_gemma":0.01387229,"domain_scores_codex":[0.999544,0.0000751707,0.00004324998,0.0001372994,0.0001279647,0.00007233779],"domain_scores_gemma":[0.9993615,0.0002691488,0.0001038675,0.00009620621,0.0001363014,0.00003299091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000957967,0.0001958312,0.01069916,0.0003670375,0.0003173691,0.000823589,0.0004339099,0.4034166,0.07145486,0.002529471,0.007490086,0.5013141],"study_design_scores_gemma":[0.00001728853,0.00008184076,0.003058198,0.00003262459,0.00003621614,0.0004249993,0.00004004173,0.975409,0.01717507,0.002175733,0.001505654,0.00004338467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1919903,0.00125242,0.7945095,0.0003990378,0.0000921067,0.0002044584,0.001637585,0.008072054,0.001842498],"genre_scores_gemma":[0.7101696,0.0007181574,0.2816531,0.0003586512,0.00004280548,0.0002118533,0.003571528,0.0006201478,0.002654121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008471617,"threshold_uncertainty_score":0.01684457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1114175915888767,"score_gpt":0.3705622071393935,"score_spread":0.2591446155505168,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}