{"id":"W2079423563","doi":"10.1109/icip.2010.5652017","title":"An image processing algorithm for accurate extraction of the centerline from human metaphase chromosomes","year":2010,"lang":"en","type":"article","venue":"","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Chromosome; Metaphase; Identification (biology); Computer science; Artificial intelligence; Image segmentation; Centromere; Pattern recognition (psychology); Karyotype; Image processing; Pruning; Segmentation; Feature extraction; Genetic algorithm; Image (mathematics); Computer vision; Algorithm; Biology; Machine learning; Genetics","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":[0.0009448072,0.0009745604,0.000760667,0.001670945,0.0006522307,0.0007444225,0.001008541,0.001185243,0.001915411],"category_scores_gemma":[0.001342416,0.0005443663,0.0006357139,0.001441326,0.0005180869,0.0009153848,0.0005357093,0.001112704,0.001354242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004860121,"about_ca_system_score_gemma":0.0011091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001872126,"about_ca_topic_score_gemma":0.00195008,"domain_scores_codex":[0.9996426,0.00002963709,0.00002807048,0.00009578892,0.0001743163,0.00002947918],"domain_scores_gemma":[0.9995062,0.0001274249,0.00006305803,0.00005923617,0.0002197474,0.0000243858],"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":[0.0001143965,0.00006660957,0.0005663428,0.0001726032,0.00003553547,0.0002035821,0.0001784202,0.01087265,0.2387108,0.004352085,0.003456025,0.7412709],"study_design_scores_gemma":[0.00007748331,0.0002597563,0.006054358,0.00006338997,0.000089648,0.001399065,0.00008966441,0.6689234,0.2813107,0.005480498,0.0361609,0.00009123786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003831036,0.00009787716,0.9946595,0.00004934234,0.00002480807,0.00007933715,0.00003959542,0.000900321,0.0003181768],"genre_scores_gemma":[0.01286597,0.00014425,0.9858813,0.00002154405,0.00001399432,0.0001165815,0.0001145348,0.00007449497,0.0007671228],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001915411,"threshold_uncertainty_score":0.006407738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009839307646917915,"score_gpt":0.2878523152336867,"score_spread":0.2780130075867688,"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."}}