{"id":"W1968747769","doi":"10.1155/2011/410912","title":"Unsupervised 3D Prostate Segmentation Based on Diffusion-Weighted Imaging MRI Using Active Contour Models with a Shape Prior","year":2011,"lang":"en","type":"article","venue":"Journal of Electrical and Computer Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Mount Sinai Hospital","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Magnetic resonance imaging; Pattern recognition (psychology); Diffusion MRI; Modality (human–computer interaction); Computer vision; Effective diffusion coefficient; Active contour model; Prostate; Image segmentation; Active shape model; Medicine; Radiology","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":[0.000168004,0.0001747176,0.0002365126,0.0003131686,0.00006325939,0.00009464298,0.0002581197,0.00003418541,0.000007355169],"category_scores_gemma":[0.000008768199,0.000127829,0.00004900184,0.0003574523,0.00001903105,0.0007541581,0.00005266815,0.0002729875,3.113502e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000887502,"about_ca_system_score_gemma":0.00007274424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006041841,"about_ca_topic_score_gemma":9.073012e-8,"domain_scores_codex":[0.9987674,0.00004719834,0.0003444069,0.0002101704,0.0003930571,0.0002377731],"domain_scores_gemma":[0.9992248,0.0000978467,0.0001933879,0.0001183873,0.0001800449,0.0001855777],"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":[0.0005889391,0.0006691126,0.0005321386,0.00009236663,0.0001525434,0.0005728567,0.003775356,0.02548097,0.03488543,0.001152713,0.00007386904,0.9320237],"study_design_scores_gemma":[0.001141918,0.0005583388,0.0007078031,0.0001535122,0.00001833248,0.0001098928,0.000006894135,0.9806473,0.0163137,0.0001796963,0.000003401356,0.0001592661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05492304,0.00004617812,0.9446151,0.00007532314,0.00006791061,0.0001741164,4.733206e-7,0.00007779542,0.00002010438],"genre_scores_gemma":[0.3601575,0.00001410189,0.6394372,0.0003190129,0.00005491289,0.000003823376,5.058611e-7,0.00001149895,0.000001442851],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9551663,"threshold_uncertainty_score":0.5212716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01276140410828498,"score_gpt":0.2167433715965962,"score_spread":0.2039819674883113,"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."}}