{"id":"W2787868188","doi":"10.1109/crv.2017.15","title":"Leveraging Tree Statistics for Extracting Anatomical Trees from 3D Medical Images","year":2017,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Prior probability; Tree (set theory); Computer science; Artificial intelligence; Pattern recognition (psychology); Segmentation; Ground truth; Noise (video); Tree structure; Bayesian probability; Computer vision; Image (mathematics); Mathematics; Binary tree; Algorithm","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":[0.0005625566,0.0001323247,0.00019628,0.00005406117,0.0003809451,0.0006326945,0.001634758,0.00008854948,0.000451685],"category_scores_gemma":[0.002657085,0.0001131835,0.00004845851,0.00003081335,0.0001647882,0.0008205496,0.0003854243,0.0001848997,0.00002273193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003648945,"about_ca_system_score_gemma":0.0001159744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002797029,"about_ca_topic_score_gemma":0.00006479759,"domain_scores_codex":[0.9983289,0.00004904618,0.0003268277,0.0004110557,0.0006033831,0.0002807563],"domain_scores_gemma":[0.997772,0.0009719619,0.0001938911,0.0007139065,0.00009486856,0.000253396],"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":[0.000004580325,0.00004803358,0.0007823974,0.000007263205,0.00001747449,0.00004806267,0.0001729534,1.735211e-7,0.003334889,0.003072691,0.02682861,0.9656829],"study_design_scores_gemma":[0.002909523,0.0001690304,0.03669765,0.0001781824,0.00004322452,0.00002919873,0.0002204996,0.5559442,0.3525602,0.04707244,0.003267829,0.0009080024],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008815007,0.00002232727,0.993605,0.002557949,0.0002903974,0.0001662748,0.00002174204,0.0003227368,0.002132123],"genre_scores_gemma":[0.08271568,0.00001787511,0.9157107,0.0009250393,0.0001715961,0.00002816688,0.00001548475,0.00001124721,0.0004041458],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9647748,"threshold_uncertainty_score":0.6101087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03916233929137122,"score_gpt":0.3539422692585025,"score_spread":0.3147799299671312,"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."}}