{"id":"W2124289575","doi":"10.5430/jbgc.v3n3p29","title":"Solving the over segmentation problem in applications of Watershed Transform","year":2013,"lang":"en","type":"article","venue":"Journal of Biomedical Graphics and Computing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Watershed; Artificial intelligence; Segmentation; Fuzzy logic; Cluster analysis; Computer science; Image segmentation; Pattern recognition (psychology); Image (mathematics); Computer vision; Scale-space segmentation; Segmentation-based object categorization","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009527053,0.00006520127,0.0001446079,0.0002404944,0.00006362331,0.0000708275,0.0003632149,0.00004535751,0.00001051957],"category_scores_gemma":[0.0000197632,0.00003985847,0.00005134623,0.0005406803,0.0001634757,0.0002770928,0.00006705389,0.0002194764,2.358104e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001367999,"about_ca_system_score_gemma":0.00003693347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004013471,"about_ca_topic_score_gemma":0.000001418764,"domain_scores_codex":[0.9987125,0.00005853963,0.000600571,0.00009583122,0.0004037547,0.0001288014],"domain_scores_gemma":[0.9992353,0.0001677871,0.0002996176,0.00009389235,0.0001122217,0.00009120646],"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":[0.000003272602,0.0001634124,0.00166446,0.0001000139,0.00003309554,0.000004275845,0.00297216,0.000006561729,0.02166991,0.004901999,0.0005378447,0.967943],"study_design_scores_gemma":[0.005632063,0.001678183,0.05442604,0.001422642,0.00008870437,0.0003949522,0.003277067,0.6536862,0.06537791,0.2114308,0.001780023,0.0008053595],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03587129,0.0001212545,0.9611613,0.00253148,0.00003791777,0.0002307073,2.58712e-7,0.00001137827,0.00003446064],"genre_scores_gemma":[0.8559186,0.0001049313,0.1435436,0.0003772053,0.00004211622,0.000007116052,8.379017e-7,0.000003214057,0.000002327614],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9671376,"threshold_uncertainty_score":0.1625381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008105029847496515,"score_gpt":0.2589677807576537,"score_spread":0.2508627509101572,"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."}}