{"id":"W2330616591","doi":"10.1117/12.2216486","title":"Ultrafast superpixel segmentation of large 3D medical datasets","year":2016,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Computer science; Segmentation; CUDA; Wavefront; Field-programmable gate array; Thread (computing); Acceleration; Real-time computing; Parallel computing; Artificial intelligence; Computer hardware","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":[0.0006259191,0.0005489659,0.000685951,0.0008241746,0.000350829,0.0009810018,0.001030313,0.0008699404,0.004793138],"category_scores_gemma":[0.001574395,0.0005536125,0.0005442369,0.0009243745,0.0003442306,0.0008658296,0.000977458,0.0007425747,0.001105227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007530703,"about_ca_system_score_gemma":0.0007843228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003227133,"about_ca_topic_score_gemma":0.01088291,"domain_scores_codex":[0.9996585,0.00007144392,0.0000191805,0.00007405254,0.0001436748,0.00003315661],"domain_scores_gemma":[0.9992554,0.0003917155,0.00005167177,0.0001666093,0.0000819104,0.00005261257],"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":[0.0006106237,0.0001583192,0.00325906,0.0004864334,0.0002238868,0.0004763847,0.0005403092,0.1590975,0.34297,0.01108626,0.01133833,0.4697529],"study_design_scores_gemma":[0.00002035783,0.00006591794,0.003237605,0.00001810123,0.00001536363,0.0003237013,0.00006670482,0.9244487,0.05661207,0.007709158,0.007458815,0.00002355142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05609573,0.0004619505,0.932421,0.0003317737,0.0000331273,0.000095394,0.0005237467,0.007616256,0.002421161],"genre_scores_gemma":[0.1724871,0.000246236,0.8237521,0.00009821688,0.00002127525,0.00008971365,0.0007772386,0.0006193608,0.001908689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004793138,"threshold_uncertainty_score":0.01603466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01068991130847919,"score_gpt":0.2604559564364401,"score_spread":0.2497660451279609,"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."}}