{"id":"W1847550145","doi":"10.1007/978-3-642-33718-5_42","title":"Segmentation with Non-linear Regional Constraints via Line-Search Cuts","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Unary operation; Maxima and minima; Computer science; Gradient descent; Mathematical optimization; Segmentation; Line (geometry); Submodular set function; Energy (signal processing); Line segment; Image segmentation; Class (philosophy); Line search; Minification; Algorithm; Descent direction; Mathematics; Artificial intelligence; Combinatorics; Artificial neural network; Geometry","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":[0.00166634,0.002057135,0.002404015,0.002154358,0.0006347302,0.003132574,0.003198405,0.002819541,0.007949072],"category_scores_gemma":[0.003979146,0.002522169,0.001878387,0.003007997,0.001312096,0.002845275,0.002030609,0.002662199,0.003290395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001117172,"about_ca_system_score_gemma":0.001672197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003180613,"about_ca_topic_score_gemma":0.006475517,"domain_scores_codex":[0.9982933,0.0002971379,0.0001259117,0.0005060356,0.0006643097,0.0001132532],"domain_scores_gemma":[0.9979533,0.001055727,0.0002037094,0.0003390432,0.0003970914,0.00005113346],"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":[0.000509219,0.0001467151,0.0004575429,0.00133705,0.0002657018,0.0003163223,0.0002897073,0.3209787,0.08742127,0.03778688,0.01314609,0.5373448],"study_design_scores_gemma":[0.00004019884,0.0000799884,0.0003596947,0.00006741444,0.00007203849,0.0003443743,0.00003675947,0.942413,0.02931066,0.01681416,0.01041531,0.00004630221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001153137,0.0001887438,0.9971619,0.00003881113,0.00001421407,0.00004048594,0.00007000563,0.0005301267,0.0008026281],"genre_scores_gemma":[0.02326558,0.000253509,0.973127,0.00004597554,0.00003117457,0.0001137987,0.0003848057,0.0006052234,0.002172952],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007949072,"threshold_uncertainty_score":0.02659231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0314667443398586,"score_gpt":0.3019853726781258,"score_spread":0.2705186283382672,"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."}}