{"id":"W4321021200","doi":"10.1109/lra.2023.3244415","title":"A Hybrid Approach to 3D Shape Estimation of Catheters Using Ultrasound Images","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Computer science; 3D ultrasound; Artificial intelligence; Robustness (evolution); Imaging phantom; Kalman filter; Visualization; Ultrasound; Catheter; Radiology; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002832951,0.0001000081,0.0001308796,0.0002530362,0.00007428827,0.0001271754,0.0002193854,0.00002249159,0.000001956317],"category_scores_gemma":[0.00006729663,0.0000990714,0.0000300085,0.0003801589,0.00005621364,0.0004115754,0.00004944827,0.00005398786,0.000009899529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003237714,"about_ca_system_score_gemma":0.00001742357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001113361,"about_ca_topic_score_gemma":4.866143e-8,"domain_scores_codex":[0.9990081,0.00004729361,0.0002628767,0.0002304105,0.0002912912,0.0001599868],"domain_scores_gemma":[0.999456,0.00009972668,0.000118784,0.0002067733,0.00004095255,0.00007776828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000137937,0.00004415034,0.00005276577,0.0001141882,0.00001944737,0.000004944604,0.001064624,0.5299864,0.4203857,0.0003941102,0.004881839,0.04305043],"study_design_scores_gemma":[0.0000955785,0.00001740254,0.0008097589,0.00003335036,0.000007620241,0.00001424554,0.00001368693,0.9352214,0.0635538,0.0001238979,0.000003177612,0.0001061283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09837081,0.000003580704,0.900124,0.0008356725,0.0001202162,0.0001949795,0.000003667494,0.0003217972,0.000025284],"genre_scores_gemma":[0.3202303,0.000004467149,0.6786362,0.001078816,0.00001646436,0.00000943707,0.0000104269,0.000007648739,0.000006310189],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.405235,"threshold_uncertainty_score":0.4040014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02401800965728595,"score_gpt":0.2792205530442587,"score_spread":0.2552025433869727,"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."}}