{"id":"W2099149651","doi":"10.1109/titb.2010.2052060","title":"Automatic Segmentation of Spinal Cord MRI Using Symmetric Boundary Tracing","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Information Technology in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institute of Neurological Disorders and Stroke","keywords":"Tracing; Segmentation; Spinal cord; Artificial intelligence; Computer vision; Computer science; Construct (python library); Boundary (topology); Surgical planning; Active contour model; Pattern recognition (psychology); Physical medicine and rehabilitation; Image segmentation; Medicine; Radiology; Psychology; Mathematics; Neuroscience","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":[0.0005967629,0.0006051839,0.0006569802,0.00157519,0.0004256551,0.0008858762,0.001070669,0.001018705,0.001501092],"category_scores_gemma":[0.001926902,0.000523079,0.0005034188,0.0008766605,0.0006544623,0.001107336,0.0009005474,0.0006462705,0.001053526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003709951,"about_ca_system_score_gemma":0.001378774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001973329,"about_ca_topic_score_gemma":0.002626803,"domain_scores_codex":[0.9993326,0.0000897566,0.00004658363,0.0001225425,0.0003666676,0.00004179833],"domain_scores_gemma":[0.9991897,0.0002578294,0.0001393777,0.0001386235,0.0002306645,0.00004380921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001300612,0.00009153639,0.001026545,0.0002281444,0.00003565327,0.000317939,0.0001883218,0.05840388,0.4650658,0.008445283,0.002141398,0.4639254],"study_design_scores_gemma":[0.00004418895,0.0001245015,0.001694276,0.00003853644,0.00002534568,0.0007268707,0.000026641,0.783701,0.1977874,0.007911819,0.007869086,0.00005030238],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01331461,0.0002191602,0.9842463,0.0000766107,0.00002304238,0.00005717431,0.00003634608,0.001100642,0.0009261117],"genre_scores_gemma":[0.1208679,0.0003509056,0.8765621,0.00007166171,0.00003661798,0.00009641427,0.0001569178,0.0002959066,0.001561591],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001973329,"threshold_uncertainty_score":0.005021632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01362929853441302,"score_gpt":0.3041472234858991,"score_spread":0.290517924951486,"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."}}