{"id":"W2060776942","doi":"10.1007/s00138-013-0497-x","title":"3D segmentation of abdominal CT imagery with graphical models, conditional random fields and learning","year":2013,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Conditional random field; CRFS; Graphical model; Artificial intelligence; Computer science; Segmentation; Cut; Inference; Markov random field; Discriminative model; Machine learning; Image segmentation; Pattern recognition (psychology); Structured prediction; Scale-space segmentation; Belief propagation; Algorithm","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":[0.001743977,0.000838881,0.001168993,0.002336429,0.0004242587,0.001574985,0.001449703,0.001825713,0.001324568],"category_scores_gemma":[0.005854444,0.001134784,0.002227296,0.001634845,0.001173779,0.00140252,0.001203783,0.001491539,0.0004246162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001376626,"about_ca_system_score_gemma":0.001383088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01480368,"about_ca_topic_score_gemma":0.01440685,"domain_scores_codex":[0.9992273,0.0003460032,0.00004505781,0.0001509851,0.0001654103,0.00006525962],"domain_scores_gemma":[0.9975617,0.001721407,0.0003168358,0.0001621257,0.0001626386,0.00007525639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001204649,0.00004744288,0.0007375548,0.00009030972,0.00005823843,0.00005829567,0.00005147087,0.9208182,0.002629394,0.01298124,0.0009779213,0.06142954],"study_design_scores_gemma":[0.000003599881,0.000006057702,0.0001180014,0.000004893742,0.000004885367,0.00001615304,0.000002118359,0.9944054,0.0003693582,0.004957493,0.0001059109,0.000006188523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007231714,0.0002148942,0.9915012,0.0001755316,0.00001337109,0.00002357592,0.00009089817,0.0005535794,0.000195246],"genre_scores_gemma":[0.4560148,0.0007664175,0.5396771,0.0002239572,0.000117287,0.0001895641,0.0008439418,0.0003941061,0.001772758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01480368,"threshold_uncertainty_score":0.02943504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006238367072077861,"score_gpt":0.2636565299567762,"score_spread":0.2574181628846983,"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."}}