{"id":"W3183868427","doi":"10.1016/j.nicl.2021.102766","title":"Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning","year":2021,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Canada Foundation for Innovation; National Natural Science Foundation of China; Canada First Research Excellence Fund; Canada Research Chairs; Nvidia; National Science Foundation","keywords":"Segmentation; Spinal cord; Medicine; Deep learning; Lumbar; Minimum bounding box; Cord; Artificial intelligence; Radiology; Computer science; Surgery","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":[0.0008602615,0.001398245,0.001030021,0.001341582,0.0004324018,0.0009177165,0.001494427,0.001527414,0.001237335],"category_scores_gemma":[0.001300377,0.0006730641,0.001281168,0.0009739037,0.0003681584,0.0008492221,0.0009997311,0.001142873,0.000865073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001420098,"about_ca_system_score_gemma":0.00141744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01831684,"about_ca_topic_score_gemma":0.02500159,"domain_scores_codex":[0.9995202,0.00007586707,0.00002891497,0.0001904377,0.00008168603,0.0001028337],"domain_scores_gemma":[0.9995793,0.000132377,0.00005583678,0.00006835148,0.000124599,0.00003953915],"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":[0.0007576407,0.0003920815,0.007448962,0.0001897299,0.0002871869,0.000406736,0.0001549693,0.3545045,0.03121253,0.001044527,0.008729781,0.5948714],"study_design_scores_gemma":[0.000008181898,0.00005059916,0.0008655282,0.00001207642,0.00001983416,0.00005270807,0.00001390851,0.9932256,0.004513135,0.0008104641,0.0004178472,0.00001008197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.341842,0.002785213,0.6350421,0.0007289473,0.0002112811,0.000317601,0.001702022,0.01429456,0.003076199],"genre_scores_gemma":[0.8256463,0.0005009763,0.1645079,0.0003829308,0.00006557556,0.0001858735,0.003413494,0.0002695979,0.005027359],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01831684,"threshold_uncertainty_score":0.03642046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02793730199582332,"score_gpt":0.3311391495736721,"score_spread":0.3032018475778487,"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."}}