{"id":"W2941524851","doi":"10.1109/tmi.2019.2930068","title":"Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks","year":2019,"lang":"en","type":"preprint","venue":"IEEE Transactions on Medical Imaging","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Prostate Cancer Canada","keywords":"Hausdorff distance; Segmentation; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Scale-space segmentation; Computer science; Image segmentation; Segmentation-based object categorization; Computer vision; Similarity (geometry); Boundary (topology); Mathematics; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002939232,0.0009214226,0.00086818,0.001385235,0.0003367088,0.00103952,0.0008632788,0.00100215,0.0005419561],"category_scores_gemma":[0.009043915,0.0004441254,0.0005771509,0.0007338924,0.001059717,0.001705629,0.001265157,0.0008694241,0.0001647148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002063768,"about_ca_system_score_gemma":0.0009770878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003365411,"about_ca_topic_score_gemma":0.00276841,"domain_scores_codex":[0.9987652,0.0003106221,0.0001283682,0.0001937028,0.0005161356,0.00008600012],"domain_scores_gemma":[0.9972121,0.0015925,0.0003518162,0.0003035612,0.0004564313,0.00008359683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004671155,0.0001022298,0.003845349,0.0002726216,0.0001602395,0.0001927856,0.0001665374,0.6800926,0.03149165,0.01088675,0.00150397,0.2708181],"study_design_scores_gemma":[0.000007358075,0.00008176966,0.001494498,0.00001681601,0.0000199261,0.000107969,0.00001583257,0.9724354,0.02123474,0.003793352,0.000771871,0.00002034703],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0887183,0.00129915,0.9073627,0.0003162573,0.00005535061,0.00006366181,0.00009178009,0.001001663,0.001091087],"genre_scores_gemma":[0.6248031,0.001028936,0.3713318,0.0001913468,0.00006214946,0.0001300418,0.0003388471,0.000264151,0.001849668],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003365411,"threshold_uncertainty_score":0.01554435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01141282055493615,"score_gpt":0.2807220776232556,"score_spread":0.2693092570683195,"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."}}