{"id":"W2280367348","doi":"10.1007/s10278-015-9844-y","title":"Sequential Registration-Based Segmentation of the Prostate Gland in MR Image Volumes","year":2015,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University; University of Waterloo; University of Toronto; Sunnybrook Health Science Centre","funders":"FedDev Ontario","keywords":"Prostate gland; Prostate; Computer vision; Artificial intelligence; Segmentation; Computer science; Image registration; Image segmentation; Magnetic resonance imaging; Image (mathematics); Medicine; Radiology; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006709715,0.0004937763,0.0006046552,0.001744823,0.0003767214,0.001084634,0.0008508838,0.0006283646,0.001638296],"category_scores_gemma":[0.001722887,0.0006664422,0.0007396152,0.001635528,0.0004115755,0.0005930931,0.0007528469,0.0006302245,0.0007616056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004229455,"about_ca_system_score_gemma":0.00128034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004904393,"about_ca_topic_score_gemma":0.007502066,"domain_scores_codex":[0.9995919,0.00008567942,0.00003225492,0.00008739797,0.0001613992,0.00004136566],"domain_scores_gemma":[0.9995503,0.0001632307,0.0000681181,0.00008919252,0.0001048455,0.00002426096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001212779,0.0001800021,0.004530639,0.0005328176,0.0001990983,0.0003371137,0.0005680145,0.0803158,0.3851212,0.004629046,0.002406784,0.5199667],"study_design_scores_gemma":[0.00008174181,0.0004682848,0.0158339,0.0000556559,0.0002065501,0.002393737,0.0002047432,0.7230331,0.2417842,0.005236338,0.01062204,0.00007963997],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1366371,0.001070942,0.8571467,0.0001577249,0.00005245311,0.0001766574,0.0002787976,0.00258046,0.001899166],"genre_scores_gemma":[0.4601952,0.000841172,0.5342394,0.00005705748,0.00005988573,0.0001230661,0.0006012192,0.0008406956,0.003042245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004904393,"threshold_uncertainty_score":0.009751678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01987857658412615,"score_gpt":0.2890450480033686,"score_spread":0.2691664714192424,"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."}}