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There has been little clinical research to examine the effects of patient positioning and pelvic motion on the alignment of the acetabular implant during total hip replacement surgery. Until now, no tools were capable of accurately measuring these variables during the actual procedure. As part of a broader program in medical robotics and computer assisted(More)
During the past year our group has been developing HipNav, a system which helps surgeons determine optimal, patient-specific acetabular implant placement and accurately achieve the desired implant placement during surgery. HipNav includes three components: a pre-operative planner, a range of motion simulator, and an intra-operative tracking and guidance(More)
An automatic, neural network-based approach was applied to segment normal brain compartments and lesions on MR images. Two supervised networks, backpropagation (BPN) and counterpropagation, and two unsupervised networks, Kohonen learning vector quantizer and analog adaptive resonance theory, were trained on registered T2-weighted and proton density images.(More)
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