We present a method for automatically determining the position and orientation of the articular marginal plane (AMP) of the proximal humerus in computed tomography (CT) images without segmentation or hand-crafted features. The process is broken down into 3 stages. Stage 1 determines a coarse estimation of the AMP center by sampling patches over the entire image and combining predictions with a novel kernel density estimation method. Stage 2 utilizes the estimate from stage 1 to focus on a smaller sampling region and operates at a higher images resolution to obtain a refined prediction of the AMP center. Stage 3 focuses patch sampling on the region around the center obtained at stage 2 and regresses the tip of a vector normal to the AMP which yields the orientation of the plane. The system was trained and evaluated on 27 upper arm CTs. In a 4-fold cross-validation the mean error in estimating the AMP center was 1.30±0.65 mm and the angular error for estimating the normal vector was 4.68±2.84∘.
CITATION STYLE
Kulyk, P., Vlachopoulos, L., Fürnstahl, P., & Zheng, G. (2019). Fully automatic planning of total shoulder arthroplasty without segmentation: A deep learning based approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11404 LNCS, pp. 22–34). Springer Verlag. https://doi.org/10.1007/978-3-030-11166-3_3
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