{"id":"W2121814445","doi":"10.1109/iembs.2008.4649855","title":"Improved interactive medical image segmentation using Enhanced Intelligent Scissors (EIS)","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Image segmentation; Pruning; Boundary (topology); Computer vision; Market segmentation; Tracing; Viterbi algorithm; Pattern recognition (psychology); Hidden Markov model; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003879375,0.0001696765,0.000186067,0.0001759944,0.0001546111,0.00009157067,0.0007446043,0.00008519956,0.001149388],"category_scores_gemma":[0.0003748003,0.0001464295,0.00007495003,0.0004024731,0.0001989103,0.001375648,0.0003336188,0.0002318153,0.00008913651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001876311,"about_ca_system_score_gemma":0.0002085087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001476517,"about_ca_topic_score_gemma":0.000009078084,"domain_scores_codex":[0.9979101,0.0001242883,0.0004520862,0.0004539199,0.0007682822,0.000291257],"domain_scores_gemma":[0.9988431,0.0001549829,0.0001537605,0.0003632039,0.0001960218,0.0002888849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001704236,0.0001967934,0.00002775827,0.00001433818,0.00003374135,0.00007508921,0.003322892,0.000004064013,0.7614962,0.0003009115,0.001411651,0.2330995],"study_design_scores_gemma":[0.0002558924,0.00007685136,0.00004752554,0.00002760539,0.000003396484,0.00006257286,0.000186301,0.1111178,0.8878427,0.0001989724,0.000021166,0.0001592557],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02895949,0.0000129994,0.9681117,0.0003415422,0.0003219156,0.0003184694,6.684636e-7,0.0004703677,0.001462845],"genre_scores_gemma":[0.2722408,0.0000619075,0.7256435,0.001610363,0.00006190535,0.00003199307,0.000005552717,0.00001236418,0.0003315615],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2432813,"threshold_uncertainty_score":0.9997637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02822095262967533,"score_gpt":0.335990562534863,"score_spread":0.3077696099051876,"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."}}