{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002687377,0.001289637,0.001485645,0.005426372,0.0004349322,0.002541632,0.002265669,0.003332855,0.00386372],"category_scores_gemma":[0.003529845,0.0007151401,0.00121196,0.004306436,0.00226975,0.004200985,0.001481344,0.004117026,0.005155565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001666444,"about_ca_system_score_gemma":0.001907432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002948761,"about_ca_topic_score_gemma":0.001841625,"domain_scores_codex":[0.9990114,0.0001962963,0.0001109396,0.0001678517,0.0004463228,0.00006708156],"domain_scores_gemma":[0.9975083,0.001247899,0.0001572064,0.000114552,0.0008583044,0.0001136809],"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":[0.00004674722,0.00007620652,0.000171322,0.00738351,0.0001011359,0.0001154594,0.0001017873,0.0009310507,0.001877066,0.01270901,0.02880074,0.9476861],"study_design_scores_gemma":[0.00001615787,0.000132877,0.001169244,0.003773411,0.00009119118,0.001989701,0.00008659651,0.001560317,0.002363164,0.01639258,0.972343,0.00008184247],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002207218,0.9884433,0.005847865,0.001532721,0.0009407029,0.00001529144,0.00002852954,0.0001070267,0.002863859],"genre_scores_gemma":[0.002601918,0.9852433,0.008011922,0.001053128,0.00111078,0.00002945565,0.0001108207,0.00006112136,0.001777475],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005426372,"threshold_uncertainty_score":0.01421237,"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."}}