{"id":"W2073649956","doi":"10.1117/12.910907","title":"Two and three-dimensional segmentation of hyperpolarized &lt;sup&gt;3&lt;/sup&gt;He magnetic resonance imaging of pulmonary gas distribution","year":2012,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Nuclear medicine; Segmentation; Magnetic resonance imaging; Nuclear magnetic resonance; Physics; Mathematics; Artificial intelligence; Medicine; Radiology; Computer 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009878526,0.0003682453,0.0004645563,0.001375713,0.0002704144,0.000954864,0.0005566019,0.00102431,0.001333131],"category_scores_gemma":[0.00172593,0.0005120629,0.0004598349,0.0005943187,0.0004615157,0.0004445861,0.0006200289,0.000467694,0.0004641266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003789113,"about_ca_system_score_gemma":0.0009112552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001915636,"about_ca_topic_score_gemma":0.004164157,"domain_scores_codex":[0.9996265,0.00006813141,0.00004147279,0.00009590144,0.0001278281,0.00004012894],"domain_scores_gemma":[0.9992918,0.0002680928,0.00007543206,0.0001291411,0.0001974228,0.00003817024],"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":[0.0003923993,0.00007460322,0.004569089,0.000349634,0.00006358839,0.0005718714,0.0005247141,0.01408808,0.845651,0.002654305,0.001071525,0.1299893],"study_design_scores_gemma":[0.00008559319,0.0003832581,0.07968763,0.00009636495,0.0001275942,0.003692491,0.000444331,0.3421914,0.5537608,0.004451629,0.01484695,0.0002319424],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2048937,0.0004961591,0.7905381,0.000121378,0.00003656538,0.000289335,0.0006562819,0.001448896,0.001519521],"genre_scores_gemma":[0.3531316,0.0003759694,0.6426917,0.00008976394,0.00001847544,0.0004824645,0.0007002163,0.0003379537,0.002171947],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001915636,"threshold_uncertainty_score":0.005224347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008815279498884415,"score_gpt":0.235730284615138,"score_spread":0.2269150051162536,"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."}}