{"id":"W2979374073","doi":"10.1109/embc.2019.8857835","title":"Liver Segmentation in Abdominal CT Images Using Probabilistic Atlas and Adaptive 3D Region Growing","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Bone and Joint Health Institute; University of Calgary","funders":"","keywords":"Artificial intelligence; Segmentation; Voxel; Computer science; Atlas (anatomy); Computer vision; Image segmentation; Region growing; Probabilistic logic; Dice; Pattern recognition (psychology); Scale-space segmentation; Contrast (vision); Mathematics; Statistics","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":[0.00006529652,0.0000820369,0.0000856945,0.00005870197,0.00005261317,0.00004401777,0.0001452454,0.00001345424,0.000004065569],"category_scores_gemma":[0.000007611957,0.00007605404,0.00001331609,0.0002586011,0.00003100483,0.0009940533,0.0001217187,0.00007114259,0.00001619616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007173845,"about_ca_system_score_gemma":0.00001531861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005212453,"about_ca_topic_score_gemma":0.00001287108,"domain_scores_codex":[0.9992833,0.00003630515,0.0001274142,0.0003154887,0.00009323772,0.0001442402],"domain_scores_gemma":[0.9995786,0.0001180025,0.00005610798,0.0001893638,0.00002631294,0.00003159183],"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":[0.0001123648,0.0002592731,0.02439201,0.0001484823,0.0000300082,0.0002001256,0.002218241,0.1267256,0.123087,0.2819993,0.0003085388,0.4405192],"study_design_scores_gemma":[0.000333846,0.00005267256,0.005073408,0.0000417128,0.000004607313,0.00008927431,0.00006667391,0.9840344,0.003505711,0.006599397,0.00003615713,0.000162106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3406548,0.00004881914,0.6583919,0.0001158056,0.00003274297,0.0003436953,1.657151e-7,0.00004163218,0.000370438],"genre_scores_gemma":[0.7228434,0.00001257118,0.2769169,0.0000951928,0.00001479915,0.00001414996,5.657303e-7,0.000004411286,0.00009799883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8573089,"threshold_uncertainty_score":0.3101394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02780360267360252,"score_gpt":0.2653911686282064,"score_spread":0.2375875659546039,"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."}}