{"id":"W4386594373","doi":"10.1080/13682199.2023.2256504","title":"Multi-object 3D segmentation of brain structures using a geometric deformable model with a priori knowledge","year":2023,"lang":"en","type":"article","venue":"The Imaging Science Journal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; A priori and a posteriori; Computer vision; Object (grammar); Probabilistic logic; Pattern recognition (psychology); Image segmentation","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":[],"consensus_categories":[],"category_scores_codex":[0.0033232,0.0001222285,0.0001386036,0.001068297,0.0006912736,0.0004938343,0.001525719,0.00001438592,0.000006446064],"category_scores_gemma":[0.0002655006,0.00007319148,0.00003983074,0.005161613,0.0005413854,0.002289418,0.000311484,0.0002583972,0.000004587381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000187278,"about_ca_system_score_gemma":0.0008311344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002562443,"about_ca_topic_score_gemma":0.00000231172,"domain_scores_codex":[0.9979211,0.00007537067,0.0003314568,0.000246342,0.0009923026,0.0004334159],"domain_scores_gemma":[0.9987293,0.0001095551,0.000328841,0.0003314107,0.0003637775,0.000137119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001407135,0.00007150439,0.00159713,0.00004087277,0.00002051696,0.00003105097,0.01288744,0.06425656,0.6299489,0.0002386892,0.0009171563,0.2899761],"study_design_scores_gemma":[0.0003139731,0.00003157029,0.001488096,0.00004889402,0.000007400446,0.0003151442,0.0003647012,0.9029613,0.09354428,0.0008275073,0.000003136067,0.00009406653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09743731,0.00006654397,0.9016069,0.0003891005,0.0001464556,0.000158128,8.251164e-7,0.0001196099,0.00007514197],"genre_scores_gemma":[0.368028,0.00001329035,0.6316183,0.0002276314,0.00002822929,0.000003201352,2.724764e-7,0.000007480276,0.00007360882],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8387046,"threshold_uncertainty_score":0.5316787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03769657898039932,"score_gpt":0.3503620714422515,"score_spread":0.3126654924618522,"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."}}