{"id":"W3171478700","doi":"10.1371/journal.pone.0251914","title":"A hybrid level set model for image segmentation","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Sichuan Province Science and Technology Support Program; Sichuan University; Sichuan University of Science and Engineering","keywords":"Active contour model; Maxima and minima; Artificial intelligence; Level set (data structures); Image segmentation; Computer science; Segmentation; Smoothing; Computer vision; Position (finance); Pattern recognition (psychology); Level set method; Process (computing); Image (mathematics); Enhanced Data Rates for GSM Evolution; Energy (signal processing); Iterative and incremental development; Edge detection; Image processing; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007312755,0.000768305,0.001139455,0.001242681,0.0004624111,0.00165298,0.002178842,0.002036921,0.003495485],"category_scores_gemma":[0.001692398,0.0007318293,0.001755065,0.001370492,0.0008213624,0.001591504,0.001083359,0.001675603,0.001610584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001151867,"about_ca_system_score_gemma":0.0009169386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003392368,"about_ca_topic_score_gemma":0.002960194,"domain_scores_codex":[0.9994428,0.0001157577,0.00002987755,0.000120847,0.0002595147,0.00003121891],"domain_scores_gemma":[0.9996154,0.000183485,0.00003735436,0.00004958417,0.00009156466,0.00002268486],"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":[0.00006295007,0.0000391074,0.0002907494,0.0001369048,0.00007071069,0.0001039076,0.0000907725,0.8349018,0.01033133,0.04854656,0.002681511,0.1027437],"study_design_scores_gemma":[0.000002921406,0.000008775497,0.0000255483,0.000004494108,0.000004332415,0.00002024905,0.0000017496,0.9934497,0.000589168,0.004767046,0.001120361,0.000005581808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001034079,0.000125879,0.9977315,0.00007922817,0.00001730371,0.00001883189,0.00003299705,0.0002426406,0.000717589],"genre_scores_gemma":[0.1747255,0.0007573005,0.814494,0.0002810051,0.00008950926,0.0004533064,0.0004337743,0.0004218478,0.00834373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003495485,"threshold_uncertainty_score":0.01169354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1474922199911524,"score_gpt":0.3154208245341232,"score_spread":0.1679286045429708,"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."}}