{"id":"W4288033329","doi":"10.21203/rs.3.rs-1641094/v2","title":"Learning to Detect Boundary Information for Brain Image Segmentation","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Segmentation; Artificial intelligence; Computer science; Boundary (topology); Image segmentation; Pattern recognition (psychology); Computer vision; Scale-space segmentation; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001854425,0.0001752303,0.0001718981,0.0005773991,0.001132462,0.0008182748,0.001438856,0.00009419648,0.00005103632],"category_scores_gemma":[0.0006839168,0.0001998987,0.0001055702,0.001060506,0.00004934158,0.0009439978,0.003722258,0.001379375,0.0001305965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006373328,"about_ca_system_score_gemma":0.0003713121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002730328,"about_ca_topic_score_gemma":0.00001039479,"domain_scores_codex":[0.9970691,0.0004252292,0.0003429746,0.0005588793,0.001030005,0.0005737867],"domain_scores_gemma":[0.9972996,0.0009959049,0.0001469166,0.0008464394,0.000538877,0.0001722536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008875895,0.00004294835,0.00008141484,0.0008791012,0.00003175661,0.000006095155,0.006410196,0.09447324,0.005457181,0.008439819,0.04114139,0.8429481],"study_design_scores_gemma":[0.0006901861,0.0011907,0.001448937,0.0001944432,0.000006530776,0.000006153644,0.001490987,0.2276759,0.004454657,0.07218375,0.6898624,0.0007953242],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005445648,0.00005847141,0.9830612,0.006508024,0.0001967427,0.003662811,0.00006675455,0.0003274709,0.0006728997],"genre_scores_gemma":[0.1156624,0.0001579889,0.8548347,0.001257259,0.0005345315,0.02356157,0.001904842,0.00009341326,0.001993203],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8421528,"threshold_uncertainty_score":0.8710097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04681458175345153,"score_gpt":0.4124881130710341,"score_spread":0.3656735313175826,"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."}}