{"id":"W4307901399","doi":"10.1016/j.jmbbm.2022.105540","title":"Segmentation of trabecular bone microdamage in Xray microCT images using a two-step deep learning method","year":2022,"lang":"en","type":"article","venue":"Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials","topic":"Bone health and osteoporosis research","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Artificial intelligence; Intersection (aeronautics); Segmentation; Deep learning; Computer science; Materials science; Biomedical engineering; Pattern recognition (psychology); Computer vision; Engineering","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":[0.0003186702,0.0004966872,0.0005710095,0.001199883,0.0002531945,0.0006431855,0.0006384015,0.001138987,0.001009709],"category_scores_gemma":[0.0006239105,0.0004241724,0.0006199205,0.0005959648,0.0003130119,0.0003572311,0.0005141821,0.0004537617,0.0003039742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004473188,"about_ca_system_score_gemma":0.001079265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006495774,"about_ca_topic_score_gemma":0.01035447,"domain_scores_codex":[0.999894,0.000008543133,0.000006934109,0.000029484,0.00003412557,0.00002688758],"domain_scores_gemma":[0.9998077,0.00005950444,0.00003385789,0.00001810565,0.00006348838,0.00001735451],"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":[0.0004712319,0.000274383,0.008190412,0.0003396729,0.0001589469,0.0005289456,0.0002492249,0.2769511,0.1928732,0.003219219,0.003521859,0.5132219],"study_design_scores_gemma":[0.000006214269,0.00002317177,0.001809654,0.000009204903,0.00001520998,0.0001001404,0.00001280928,0.9876378,0.00945042,0.0005332511,0.0003931765,0.000008964633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1232916,0.0005563151,0.8732062,0.0002418495,0.0000342749,0.0001079628,0.0002693232,0.001388547,0.0009039846],"genre_scores_gemma":[0.5912166,0.0004465654,0.404151,0.0001815846,0.00004557765,0.0001312948,0.0005539774,0.0001775534,0.003095899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006495774,"threshold_uncertainty_score":0.01291591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0264266340094094,"score_gpt":0.3566778717338249,"score_spread":0.3302512377244154,"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."}}