{"id":"W4399555189","doi":"10.1016/j.wneu.2024.06.026","title":"A Practical Roadmap to Implementing Deep Learning Segmentation in the Clinical Neuroimaging Research Workflow","year":2024,"lang":"en","type":"review","venue":"World Neurosurgery","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke; Hôpital Fleurimont; Centre Hospitalier Universitaire de Sherbrooke; Université de Montréal","funders":"","keywords":"Workflow; Artificial intelligence; Machine learning; Neuroimaging; Segmentation; Iterative and incremental development; Computer science; Process (computing); Deep learning; Reliability (semiconductor); Software; Transfer of learning; Artificial neural network; Medicine; Software engineering; Data science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008573471,0.0003778833,0.0008287316,0.0009320251,0.0001846897,0.0004506918,0.0004738915,0.0001615328,0.00002391799],"category_scores_gemma":[0.00247002,0.0002898051,0.0007389627,0.002181042,0.00009193196,0.00001104138,0.0008441081,0.002249284,0.00011189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004726473,"about_ca_system_score_gemma":0.000214885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000142407,"about_ca_topic_score_gemma":0.0001506708,"domain_scores_codex":[0.9922414,0.004001088,0.001301273,0.001180076,0.0005377542,0.0007384365],"domain_scores_gemma":[0.9975108,0.001233442,0.0002639503,0.0007943641,0.00008766566,0.000109797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001340786,0.0000952921,0.0005323759,0.0008966681,0.00007138006,0.0004647964,0.000030771,0.000002010585,0.0003691154,0.00001185882,0.08115999,0.9163523],"study_design_scores_gemma":[0.0000377824,0.00006040842,0.00002655587,0.0009712703,0.0002678975,0.00006728199,0.00003342167,0.00003039588,0.0000868234,0.00001131246,0.9981555,0.000251383],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003349191,0.9942122,0.0005424941,0.001018276,0.0002888188,0.001512497,0.000002918247,0.00008563996,0.002002219],"genre_scores_gemma":[0.0008168656,0.9942447,0.0008856754,0.0008754624,0.0008566093,0.0004087173,0.0001981042,0.0001265082,0.001587354],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9169955,"threshold_uncertainty_score":0.9999554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1769590590271222,"score_gpt":0.5207263053092895,"score_spread":0.3437672462821673,"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."}}