A deep learning pipeline was developed and used to localize and classify a variety of implants in the femur contained in whole-body post-mortem computed tomography (PMCT) scans. The results provide a proof-of-principle approach for labelling content ...
Computer methods in biomechanics and biomedical engineering
May 4, 2020
Dislocation after total hip arthroplasty (THA) remains a major issue and an important post-surgical complication. Impingement and subsequent dislocation are influenced by the design (head size) and position (anteversion and abduction angles) of the a...
The international journal of medical robotics + computer assisted surgery : MRCAS
Apr 1, 2020
The objectives were to determine errors in femoral anteversion (FA), femoral offset (FO), and vertical offset (VO) with robot-assisted total hip arthroplasty (THA) and how consistently these errors are within clinically desirable limits of ±5° and ±5...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 5, 2020
Medical image segmentation is one of the most crucial issues in medical image processing and analysis. In general, segmentation of the various structures in medical images is performed for the further image analyzes such as quantification, assessment...
The complicated interplay of total knee replacement (TKR) positioning and patient-specific soft tissue conditions still causes a considerable number of unsatisfactory outcomes. Therefore, we deployed a robot-assisted test method, in which a six-axis ...
BACKGROUND: The variation in articular cartilage thickness (ACT) in healthy knees is difficult to quantify and therefore poorly documented. Our aims are to (1) define how machine learning (ML) algorithms can automate the segmentation and measurement ...
OBJECTIVE: To assess the ability of radiography-based bone texture variables in proximal femur and acetabulum to predict incident radiographic hip osteoarthritis (rHOA) over a 10 years period.
We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging (MRI) that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D...
Knowledge about the thickness of the cortical bone is of high interest for fracture risk assessment. Most finite element model solutions overlook this information because of the coarse resolution of the CT images. To circumvent this limitation, a thr...
Magnetic resonance imaging (MRI) has been proposed as a complimentary method to measure bone quality and assess fracture risk. However, manual segmentation of MR images of bone is time-consuming, limiting the use of MRI measurements in the clinical p...
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