{"id":"W2156024736","doi":"10.1109/isbi.2004.1398493","title":"A new image segmentation and smoothing model","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Smoothing; Level set (data structures); Image segmentation; Initialization; Partial differential equation; Segmentation; Computer science; Scale-space segmentation; Artificial intelligence; Computer vision; Decoupling (probability); Level set method; Active contour model; Image denoising; Noise reduction; Algorithm; Mathematics; Mathematical analysis","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":[0.0009381576,0.0005903623,0.001107738,0.00121513,0.0004211684,0.001351644,0.002083389,0.002180206,0.002405322],"category_scores_gemma":[0.001764499,0.0007084463,0.001440345,0.00100635,0.0008967927,0.002951574,0.001377426,0.001704788,0.001244277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008726029,"about_ca_system_score_gemma":0.001183592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001905801,"about_ca_topic_score_gemma":0.001547995,"domain_scores_codex":[0.9991862,0.0000658133,0.00004474163,0.0002604064,0.0003941699,0.00004857964],"domain_scores_gemma":[0.9994099,0.0001611916,0.00007276583,0.0001065543,0.00019355,0.00005600591],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001879516,0.0001319719,0.001291071,0.000358005,0.000146604,0.0005290214,0.0004769776,0.3946076,0.1078318,0.2037497,0.008356402,0.2823329],"study_design_scores_gemma":[0.00001657634,0.00004074025,0.0002762025,0.00001509274,0.00003322473,0.0002442102,0.0000139533,0.9568272,0.008013817,0.02006796,0.01441629,0.00003471209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001685878,0.0001087113,0.9968874,0.0001577349,0.00004890189,0.00001731617,0.00003010954,0.0002928542,0.0007711297],"genre_scores_gemma":[0.1021825,0.0008651437,0.8820975,0.0004644766,0.0002414932,0.0002236271,0.0004389606,0.0005019829,0.01298437],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002405322,"threshold_uncertainty_score":0.008046567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01354909603893452,"score_gpt":0.2882608604812987,"score_spread":0.2747117644423642,"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."}}