{"id":"W3172516001","doi":"10.1007/s42979-021-00704-7","title":"Biomedical Image Segmentation: A Survey","year":2021,"lang":"en","type":"article","venue":"SN Computer Science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Segmentation; Positron emission tomography; Magnetic resonance imaging; Medical imaging; Artificial intelligence; Computer vision; Image segmentation; Computer science; Market segmentation; Computed tomography; Modalities; Medical physics; Radiology; Medicine","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":[0.001726626,0.0009959333,0.001722529,0.00555932,0.0005577992,0.002338615,0.00161794,0.001697218,0.00294925],"category_scores_gemma":[0.003236563,0.000939302,0.001104005,0.006953509,0.0008296655,0.002592954,0.0008459061,0.001151045,0.002246017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006149322,"about_ca_system_score_gemma":0.001377462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002228984,"about_ca_topic_score_gemma":0.002823109,"domain_scores_codex":[0.9989315,0.0001347811,0.00012937,0.0003693448,0.0003762485,0.00005879935],"domain_scores_gemma":[0.996832,0.001856721,0.0002055762,0.0002094262,0.0007848189,0.0001115549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009268055,0.0001312251,0.002345675,0.004032187,0.0001030776,0.00009220748,0.0001226554,0.001338288,0.004423393,0.002956432,0.01003334,0.9743289],"study_design_scores_gemma":[0.00007556642,0.0008114929,0.02051638,0.004728249,0.0008027413,0.01052538,0.0009298068,0.04732803,0.03579848,0.02293247,0.855294,0.0002574275],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.009458335,0.8783905,0.09893616,0.001649473,0.000397876,0.0001105661,0.0003932845,0.0006726549,0.009991185],"genre_scores_gemma":[0.0434839,0.8584285,0.08766643,0.001228252,0.001721822,0.0001238035,0.001245797,0.0002843078,0.005817164],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00555932,"threshold_uncertainty_score":0.009866178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02360859321297869,"score_gpt":0.3174147246370729,"score_spread":0.2938061314240942,"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."}}