{"id":"W1989708985","doi":"10.1109/tpami.2010.83","title":"Decoupled Active Contour (DAC) for Boundary Detection","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":107,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Initialization; Computer science; Active contour model; Artificial intelligence; Viterbi algorithm; Boundary (topology); Image segmentation; Noise (video); Curvature; Segmentation; Maxima and minima; Computer vision; Energy (signal processing); Algorithm; Pattern recognition (psychology); Hidden Markov model; Image (mathematics); Mathematics","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":[0.001961546,0.001406057,0.001297192,0.002794932,0.0005828511,0.001747289,0.002471258,0.002997858,0.003073316],"category_scores_gemma":[0.007433701,0.001250271,0.001225074,0.002182273,0.001362464,0.002639257,0.001824618,0.002432206,0.001428415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000991027,"about_ca_system_score_gemma":0.001309821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001696484,"about_ca_topic_score_gemma":0.002178078,"domain_scores_codex":[0.9987203,0.0002944929,0.00005973762,0.000238733,0.0006257712,0.00006099088],"domain_scores_gemma":[0.99634,0.002184158,0.0003058489,0.0004276282,0.0006422136,0.0001001051],"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":[0.0002593047,0.00009938055,0.0007686059,0.000353074,0.0001189722,0.0001633159,0.000262977,0.2787036,0.03897805,0.02972428,0.004064258,0.6465042],"study_design_scores_gemma":[0.00001768274,0.0000310898,0.000158412,0.00002265145,0.00001325284,0.000119847,0.00001324736,0.9788774,0.0083378,0.008819049,0.00356446,0.00002514636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001322486,0.0002048963,0.9976694,0.00003861172,0.00001890284,0.00002602538,0.00001549525,0.0003072683,0.0003969173],"genre_scores_gemma":[0.06308816,0.0003485603,0.9341994,0.0001085395,0.00003786808,0.0001541289,0.0001363452,0.0002099714,0.00171709],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003073316,"threshold_uncertainty_score":0.01037377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01606698689230424,"score_gpt":0.2972643352973623,"score_spread":0.2811973484050581,"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."}}