{"id":"W4321794875","doi":"10.20517/ir.2023.02","title":"An overview of intelligent image segmentation using active contour models","year":2023,"lang":"en","type":"article","venue":"Intelligence & Robotics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Image segmentation; Active contour model; Pattern recognition (psychology); Computer vision; Machine learning; Data mining","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":[0.0005068178,0.0001915449,0.000266017,0.0002730913,0.00007963109,0.0001052274,0.001016262,0.00008377586,0.00005406296],"category_scores_gemma":[0.00009452096,0.0001907547,0.00008912737,0.001030035,0.0001502086,0.00159534,0.000234111,0.0001656619,0.00006670248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001308541,"about_ca_system_score_gemma":0.000119452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001131331,"about_ca_topic_score_gemma":0.000007211504,"domain_scores_codex":[0.997884,0.0001565476,0.0006084286,0.0004172333,0.000606948,0.0003268519],"domain_scores_gemma":[0.9983994,0.000159741,0.000274459,0.0006306868,0.0003651482,0.0001705501],"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":[0.00001533384,0.0002729343,0.00005603472,0.0001509899,0.00005548547,0.00004819124,0.007178807,0.2849905,0.1353751,0.04046568,0.000196872,0.5311941],"study_design_scores_gemma":[0.00002865312,0.00008811689,0.0000207209,0.000066223,0.000009753423,0.000004362619,0.0005306932,0.5496847,0.4316918,0.01774751,0.000003283836,0.0001241348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002517108,0.000128792,0.9960765,0.0001149775,0.0002648821,0.0003664927,0.000008331002,0.0004066485,0.0001162279],"genre_scores_gemma":[0.1602798,0.001102992,0.8381429,0.0003119477,0.00004824658,0.00002032574,0.00002387048,0.00002568193,0.00004426079],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.53107,"threshold_uncertainty_score":0.777875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1873420697771262,"score_gpt":0.4207724582770987,"score_spread":0.2334303884999726,"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."}}