{"id":"W2759084104","doi":"10.1002/mp.12593","title":"Esophagus segmentation in CT via 3D fully convolutional neural network and random walk","year":2017,"lang":"en","type":"article","venue":"Medical Physics","topic":"Esophageal Cancer Research and Treatment","field":"Medicine","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Educational Testing Service","keywords":"Hausdorff distance; Segmentation; Artificial intelligence; Sørensen–Dice coefficient; Convolutional neural network; Computer science; Esophagus; Pattern recognition (psychology); Deep learning; Similarity (geometry); Computer vision; Image segmentation; Image (mathematics); Medicine; Anatomy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002439791,0.000109298,0.0002419252,0.00001682043,0.0001799479,0.00002911799,0.00008923007,0.00003638036,0.0001465634],"category_scores_gemma":[0.0001354901,0.00008486344,0.0000461796,0.0000479083,0.000253518,0.0001086187,0.00007078224,0.0002611213,0.00002458176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009288509,"about_ca_system_score_gemma":0.0001466051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002367004,"about_ca_topic_score_gemma":0.00005123734,"domain_scores_codex":[0.9986742,0.00004303888,0.0001603099,0.0002020086,0.0006302436,0.0002901873],"domain_scores_gemma":[0.9992905,0.00009666716,0.00005793538,0.0002030843,0.00003577378,0.0003160594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001274539,0.0003974074,0.6851279,0.0001222229,0.0001211331,0.001935025,0.00008310651,0.00004271846,0.00002169672,0.0003502031,0.002330641,0.3081934],"study_design_scores_gemma":[0.03052115,0.0007596788,0.9444122,0.0002314324,0.00009413547,0.0001509157,0.00001277743,0.01712916,0.0009636653,0.005384221,0.0001503035,0.0001903756],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9508137,0.02743163,0.00628127,0.01059553,0.0007882139,0.001649169,0.0000242155,0.00008363821,0.00233266],"genre_scores_gemma":[0.9980679,0.0001718968,0.0001522447,0.000494463,0.000834317,0.00004893359,0.00005325496,0.00001050421,0.0001665348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.308003,"threshold_uncertainty_score":0.346063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0219144255676469,"score_gpt":0.3279589406905765,"score_spread":0.3060445151229296,"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."}}