USB Gym: A Gymnasium Testbed for Closed-Loop Ultrasound View Matching
Overview
This research introduces USB Gym, a simulation environment designed to evaluate closed-loop ultrasound guidance methods. The testbed simulates ultrasound images from public patient CT scans and integrates three replaceable components—organ segmenter, controller, and reward function—to benchmark 20 published methods. Results show that combining these methods did not significantly improve probe positioning compared to the original guide, with the best combination achieving only 13.7 mm accuracy versus a pose oracle's 1.5 mm. The authors emphasize that no method was tested on physical hardware, and the task remains under-explored.
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Event evidence and corrections
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Environment: simulation
“arXiv:2611.08352v1 Announce Type: cross Abstract: Background and Objective: Our main goal is ultrasound guidance: moving a probe until its image matches a target view. Published methods for each part of a guide are evaluated in separate simulators. We asked whether these methods, combined into one guide, are good enough. Methods: We built USB Gym, a testbed whose standard engine simulates ultrasound from public patie”
Exact source · revision 1Source owner not reported
Reported quantity · probe distance from target view: 16 other · Basis not reported
“nchmark baseline. A pose oracle, reading the exact probe pose available only in simulation, set the reference. The results below are exploratory. Results: On these CTs, the probe started on average 16.0 mm from the target view; the pose oracle brought it to 1.5 mm. Of the results known when the combination was fixed, only an image-similarity reward (SSIM) was slightly but detectably better, so the combination was our”
Exact source · revision 1Source owner not reported
Reported quantity · probe distance after oracle correction: 1.5 other · Basis not reported
“nchmark baseline. A pose oracle, reading the exact probe pose available only in simulation, set the reference. The results below are exploratory. Results: On these CTs, the probe started on average 16.0 mm from the target view; the pose oracle brought it to 1.5 mm. Of the results known when the combination was fixed, only an image-similarity reward (SSIM) was slightly but detectably better, so the combination was our”
Exact source · revision 1Source owner not reported
Reported quantity · number of episodes per patient: 6 other · Basis not reported
“nt CT, with a network drawing organ textures from CT labels. Its guide has three replaceable parts (slots), an organ segmenter, a controller and a reward, which received 20 published methods, each run on the same 60 episodes of six held-out patients. In each slot, a published method replaced ours only if detectably better on these patients. The better of this combination and our original guide on 20 new CTs is the be”
Exact source · revision 1Source owner not reported
Reported quantity · number of published methods tested: 20 other · Basis not reported
“nt CT, with a network drawing organ textures from CT labels. Its guide has three replaceable parts (slots), an organ segmenter, a controller and a reward, which received 20 published methods, each run on the same 60 episodes of six held-out patients. In each slot, a published method replaced ours only if detectably better on these patients. The better of this combination and our original guide on 20 new CTs is the be”
Exact source · revision 1Source owner not reported
Reported quantity · number of new CTs for benchmark: 20 other · Basis not reported
“nt CT, with a network drawing organ textures from CT labels. Its guide has three replaceable parts (slots), an organ segmenter, a controller and a reward, which received 20 published methods, each run on the same 60 episodes of six held-out patients. In each slot, a published method replaced ours only if detectably better on these patients. The better of this combination and our original guide on 20 new CTs is the be”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalUSB Gym: A Gymnasium Testbed for Closed-Loop Ultrasound View Matching
This research introduces USB Gym, a simulation environment designed to evaluate closed-loop ultrasound guidance methods. The testbed simulates ultrasound images from public patient CT scans and integrates three replaceable components—organ segmenter, controller, and reward function—to benchmark 20 published methods. Results show that combining these methods did not significantly improve probe positioning compared to the original guide, with the best combination achieving only 13.7 mm accuracy versus a pose oracle's 1.5 mm. The authors emphasize that no method was tested on physical hardware, and the task remains under-explored.
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