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Motivation: Aim of this project is the automatic classification of total hip endoprosthesis (THEP) components in 2D Xray images. Revision surgeries of total hip arthroplasty (THA) are common procedures in orthopedics and trauma surgery. Currently, around 400.000 procedures per year are performed in the United States (US) alone. To achieve the best possible result, preoperative planning is crucial. Especially if parts of the current THEP system are to be retained.
Methods: First, a ground truth based on 76 X-ray images was created: We used an image processing pipeline consisting of a segmentation step performed by a convolutional neural network and a classification step performed by a support vector machine (SVM). In total, 11 classes (5 pans and 6 shafts) shall be classified.
Results: The ground truth generated was of good quality even though the initial segmentation was performed by technicians. The best segmentation results were achieved using a U-net architecture. For classification, SVM architectures performed much better than additional neural networks.
Conclusions: The overall image processing pipeline performed well, but the ground truth needs to be extended to include a broader variability of implant types and more examples per training class.
This paper discusses the development and application of an augmented reality (AR) system for assisting in nail implantation procedures for complex tibial fractures. Traditional procedures involve extensive x-ray usage from various angles, leading to increased radiation exposure and prolonged surgical times. The study presents a method using pre- and post-operative computed tomography (CT) data sets and a convolutional neural network (CNN) trained on segmented bone and metal objects. The augmented reality system overlays accurate 3D representations of bony fragments and implants onto the surgeon's view, aiming to reduce radiation exposure and intervention time. The study demonstrates successful segmentation of bone and metal objects in cases of heavy metal artifacts, achieving promising results with a relatively low number of training sets. The integration of this system into the clinical workflow could potentially improve surgical outcomes, significantly reduce radiation time, and therefore improve patient safety.
