{"id":"W1990994043","doi":"10.1118/1.2409238","title":"Subvoxel precise skeletons of volumetric data based on fast marching methods","year":2007,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":123,"is_retracted":false,"has_abstract":true,"ca_institutions":"Terahertz Technology Solutions (Canada)","funders":"National Institutes of Health","keywords":"Voxel; Computer science; Skeletonization; Smoothing; Artificial intelligence; Computer vision; Representation (politics); Field (mathematics); Algorithm; Fast marching method; Object (grammar); Distance transform; Mathematics; Image (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":[0.001814033,0.0009024814,0.001034369,0.002075339,0.0005970101,0.001964427,0.001526105,0.0008765888,0.002222676],"category_scores_gemma":[0.008963172,0.00095565,0.0009123433,0.001947164,0.001255626,0.002514909,0.001901232,0.001598153,0.001324545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000718485,"about_ca_system_score_gemma":0.001395845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00228196,"about_ca_topic_score_gemma":0.002745672,"domain_scores_codex":[0.9988176,0.00020006,0.00009977568,0.0001449044,0.0006852883,0.00005235014],"domain_scores_gemma":[0.9968478,0.001357417,0.0003503179,0.0008055503,0.0005629507,0.00007598863],"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":[0.000117221,0.00004494256,0.001051212,0.0002178927,0.00005313609,0.0001396982,0.0003978411,0.3243091,0.03217157,0.06872617,0.003145352,0.5696258],"study_design_scores_gemma":[0.000017589,0.00002701973,0.0003246268,0.00002046329,0.000008940465,0.0001380592,0.00002527746,0.9527816,0.01468178,0.02563112,0.006315598,0.00002804067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001731127,0.00003081547,0.9973202,0.00002597212,0.000005019723,0.00001959888,0.00002150345,0.0006588791,0.0001869207],"genre_scores_gemma":[0.02260792,0.0001102695,0.9762936,0.00001297017,0.000008062866,0.00006827577,0.0001159025,0.0002995957,0.0004833748],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00228196,"threshold_uncertainty_score":0.009593666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05861848673465701,"score_gpt":0.4142345303478467,"score_spread":0.3556160436131897,"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."}}