{"id":"W3184111456","doi":"10.3389/fcomp.2021.592296","title":"Spine and Individual Vertebrae Segmentation in Computed Tomography Images Using Geometric Flows and Shape Priors","year":2021,"lang":"en","type":"article","venue":"Frontiers in Computer Science","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research","keywords":"Segmentation; Computer science; Artificial intelligence; Context (archaeology); Pipeline (software); Prior probability; Computer vision; Surgical planning; Vertebral column; Radiology; Medicine; Anatomy; Geology; Bayesian probability","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001296051,0.0008343183,0.0006996808,0.003550388,0.0005072397,0.001841974,0.0008590684,0.001805036,0.001398771],"category_scores_gemma":[0.004881119,0.000685769,0.001049663,0.001562462,0.0008882539,0.001029468,0.001002766,0.0008998973,0.0009684196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001041541,"about_ca_system_score_gemma":0.002144345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006832363,"about_ca_topic_score_gemma":0.00886488,"domain_scores_codex":[0.9994073,0.0001034902,0.00003962856,0.000175415,0.0002160888,0.00005797434],"domain_scores_gemma":[0.9990057,0.0004133968,0.000180778,0.0001426195,0.0002109848,0.00004643937],"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":[0.0004355534,0.0001671692,0.004295373,0.0002526224,0.00007262714,0.0003486957,0.000385862,0.366532,0.0926948,0.01225989,0.004701667,0.5178537],"study_design_scores_gemma":[0.00002478502,0.00005779123,0.00327372,0.00003815111,0.00002135007,0.0003681235,0.00004843849,0.9575698,0.02478597,0.009838175,0.003938877,0.000034789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02726018,0.0002791786,0.9691936,0.0002278388,0.00002972625,0.0001440781,0.0002318349,0.001574785,0.001058734],"genre_scores_gemma":[0.1748373,0.000439636,0.8213151,0.0001572123,0.00006902381,0.0001815685,0.0008857789,0.0005898908,0.001524468],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006832363,"threshold_uncertainty_score":0.01358521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01071255697892991,"score_gpt":0.2250386028682356,"score_spread":0.2143260458893057,"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."}}