{"id":"W1926518666","doi":"10.1007/978-3-642-15711-0_17","title":"Nonlocal Patch-Based Label Fusion for Hippocampus Segmentation","year":2010,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Pattern recognition (psychology); Image warping; Market segmentation; Fusion; Prior probability; Computer vision","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":[0.001175272,0.0001911019,0.000173186,0.0003602915,0.0002447391,0.000366395,0.001632391,0.0001144587,0.00001591904],"category_scores_gemma":[0.0003434021,0.0001676316,0.00004714515,0.001309827,0.0004147522,0.0007698219,0.000301223,0.0003447658,0.00001029587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008935959,"about_ca_system_score_gemma":0.00035973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003750666,"about_ca_topic_score_gemma":0.00008988372,"domain_scores_codex":[0.9976753,0.00005345374,0.0003277396,0.0007824688,0.00068285,0.0004781811],"domain_scores_gemma":[0.9982602,0.000541161,0.0001255072,0.0006650004,0.0002308124,0.0001773414],"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":[0.000005440696,0.00007452485,0.0003016838,0.00001340622,8.046297e-7,0.00000455239,0.0002245053,0.0005569311,0.139788,0.000138021,0.00002614543,0.858866],"study_design_scores_gemma":[0.0004458886,0.0001627885,0.0002186821,0.00002016807,0.000001223227,0.000006002447,2.820765e-7,0.6409053,0.3490732,0.009012374,0.00001311373,0.0001409355],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02359542,0.00001140188,0.9723697,0.00158043,0.001579128,0.0005666892,0.000002120792,0.0002892049,0.000005895178],"genre_scores_gemma":[0.360857,8.745902e-7,0.6362082,0.0027716,0.0001049671,0.00004664699,0.000004117742,0.000006218743,3.957623e-7],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8587251,"threshold_uncertainty_score":0.6835819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01404184570721503,"score_gpt":0.2984307847783678,"score_spread":0.2843889390711528,"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."}}