Abstract
In clinics, the reduction of femoral intertrochanteric fractures should meet the medical demands of both axis alignment and position alignment. State-of-the-art approaches are designed for merely position alignment, not allowing for axis alignment. The axis-position alignment can be formulated as a least square optimization problem with the inequality constraints. The main challenges include how to solve this constrained optimization problem and effectively extract the semantic of the randomly fractured bone pieces. To address these problems, a semi-automatic data-driven method is introduced. First, the medical semantic parameters are computed, at the beginning of when the 3D input pieces' anatomical areas are labeled by using the deep neural network. A statistical shape model is leveraged to generate the synthetic training data so as to learn the anatomical landmarks of the pieces, greatly reducing the labeling costs for training. The final reduction position of the pieces is obtained through iterative axis alignment and position alignment. Our method is evaluated by three baselines, i.e., the manual assembly of the orthopaedic specialists and two typical bone assembling methods. The presented method solves an optimization problem for assembling intertrochanteric fracture by axis-position alignment. All cases can be successfully assembled with the developed algorithm which is proved to be capable of reaching the clinical demand.
Highlights
When a fracture occurs in the human body, the treatment usually requires the assembling and fixation of the bone fragments
Extracting semantic regions is the key step in our approach
To evaluate the accuracy of extracting regions, we compared the regions extracted by our method with the regions manually labeled by orthopaedic surgeons
Summary
When a fracture occurs in the human body, the treatment usually requires the assembling and fixation of the bone fragments. The assembling of the bone fragments is called fracture reduction in clinical practice [1]. With the development and maturation of computer-aided design and computer graphics research, the preoperative use of computers for three-dimensional fracture reduction [2], [3] can improve the accuracy of intraoperative surgical reduction and shorten the operation time. The state-of-the-art of fracture reduction methods [3] only consider aligning the fracture boundaries, which we call position alignment. These methods will probably fail in the scenarios that the boundaries can be impossibly matched due to missing of pieces and the cluttered/blurred boundaries
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