{"id":"W1681294000","doi":"10.1007/978-3-642-33418-4_50","title":"Multi-Object Geodesic Active Contours (MOGAC)","year":2012,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Robarts Research Institute; Johns Hopkins University","keywords":"Computer science; Artificial intelligence; Pixel; Computer vision; Segmentation; Geodesic; Image segmentation; Object (grammar); Memory footprint; Graph; Dimension (graph theory); Segmentation-based object categorization; Scale-space segmentation; Pattern recognition (psychology); Theoretical computer science; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00119809,0.001391976,0.001367664,0.001665815,0.0006089555,0.001539,0.0018513,0.002174256,0.002292256],"category_scores_gemma":[0.002596308,0.000969624,0.001228216,0.001613153,0.0008827392,0.002131357,0.001744532,0.001355553,0.0008819061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000829168,"about_ca_system_score_gemma":0.001126274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003266699,"about_ca_topic_score_gemma":0.004047526,"domain_scores_codex":[0.9992796,0.0001456684,0.00003504597,0.000162614,0.0003305205,0.00004654923],"domain_scores_gemma":[0.9990838,0.0003524891,0.0001362479,0.0001998667,0.0001810558,0.00004660913],"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":[0.0001907589,0.00007777804,0.0009700626,0.0002856823,0.0001548219,0.0002090115,0.0002146867,0.4193212,0.02034311,0.03181666,0.00559078,0.5208254],"study_design_scores_gemma":[0.0000121542,0.00002946932,0.0001782967,0.00001476407,0.00001243422,0.0001267013,0.00001664945,0.9750933,0.006498296,0.01237992,0.005619762,0.00001821157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003834573,0.0004544724,0.9935707,0.000133608,0.00004047668,0.00004399223,0.0000387704,0.0009132192,0.0009702102],"genre_scores_gemma":[0.07505711,0.0005025906,0.921935,0.00010602,0.0000430342,0.00009874102,0.000198204,0.0002906037,0.00176879],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003266699,"threshold_uncertainty_score":0.007668316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02335241677760284,"score_gpt":0.3070234742835281,"score_spread":0.2836710575059253,"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."}}