{"id":"W1982668309","doi":"10.1118/1.4871620","title":"Vision 20/20: Perspectives on automated image segmentation for radiotherapy","year":2014,"lang":"en","type":"review","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":376,"is_retracted":false,"has_abstract":true,"ca_institutions":"Philips (Canada)","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health","keywords":"Computer vision; Image segmentation; Medical imaging; Radiation therapy; Artificial intelligence; Image-guided radiation therapy; Computer science; Segmentation; Medical physics; Image processing; Image (mathematics); Medicine; Radiology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009324035,0.0004863004,0.001272389,0.0001508976,0.0001402403,0.0002083632,0.001430047,0.0003673229,0.0001220281],"category_scores_gemma":[0.0004011134,0.000371359,0.0004892991,0.0004932703,0.000236156,0.0003738951,0.0001418455,0.0005240876,0.000116227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003382968,"about_ca_system_score_gemma":0.0003770044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002613504,"about_ca_topic_score_gemma":2.951664e-7,"domain_scores_codex":[0.996203,0.0003857242,0.0007233117,0.0008911805,0.001401911,0.0003948648],"domain_scores_gemma":[0.9972801,0.0009437598,0.0004968835,0.0007788153,0.0001561204,0.0003442799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002212525,0.0001948042,2.541164e-8,0.002073174,0.00005911604,0.000005970891,0.0002326033,2.362842e-7,0.00001416596,0.0007629709,0.04103,0.9556247],"study_design_scores_gemma":[0.001485844,0.001267882,4.222712e-7,0.01167499,0.0002048176,0.00002017922,0.00003250305,0.02810612,0.001466828,0.002514378,0.952077,0.001149016],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[5.168173e-8,0.2936338,0.7034501,0.0002806517,0.0003134745,0.001041037,0.00001786918,0.001072224,0.000190771],"genre_scores_gemma":[7.184416e-7,0.851567,0.1456318,0.0009253823,0.0008901153,0.0004473633,0.0002779564,0.00007043692,0.0001892497],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9544757,"threshold_uncertainty_score":0.9998738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02577495733737145,"score_gpt":0.4028835782834476,"score_spread":0.3771086209460762,"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."}}