{"id":"W4403204441","doi":"10.1007/978-3-031-72744-3_21","title":"How to Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-angle Maximum Intensity Projections and Diffusion Models","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Prostate cancer; Segmentation; Computer science; Intensity (physics); Diffusion; Image segmentation; Artificial intelligence; Cancer; Medicine; Optics; Physics; Internal medicine","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":[0.000667048,0.001503521,0.001121591,0.001524235,0.0004976082,0.003541314,0.001562258,0.002410786,0.01196106],"category_scores_gemma":[0.001558099,0.001810138,0.002028991,0.00169942,0.0005589225,0.002437197,0.0009698411,0.001538488,0.01290883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000545515,"about_ca_system_score_gemma":0.0008886805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004102839,"about_ca_topic_score_gemma":0.007286922,"domain_scores_codex":[0.9996737,0.00003544331,0.0000264187,0.00007838337,0.0001580438,0.00002801311],"domain_scores_gemma":[0.9994837,0.000234564,0.00004080997,0.00008750302,0.0001202825,0.00003295431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008098009,0.00005640052,0.0004916051,0.0007000844,0.0001164411,0.0002326443,0.0001885812,0.0374362,0.03525985,0.00873152,0.0559618,0.8607439],"study_design_scores_gemma":[0.00004504615,0.0001278785,0.002183499,0.0003461159,0.0001893019,0.003636564,0.0002245718,0.6506168,0.07663295,0.05811612,0.2076301,0.0002511148],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002161105,0.002414457,0.9828569,0.0006856497,0.0001937523,0.00008706964,0.0006770567,0.006875494,0.004048506],"genre_scores_gemma":[0.01096098,0.003483779,0.974546,0.0002614776,0.00008007837,0.00009151065,0.001059581,0.00260848,0.006908074],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01196106,"threshold_uncertainty_score":0.04001379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07432931767318937,"score_gpt":0.3444978140958455,"score_spread":0.2701684964226562,"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."}}