Download PDFOpen PDF in browserStatistical Analysis of Sacral Bone Density using a CNN-based Atlas Creation on a Large-scale CT database5 pages•Published: September 25, 2020AbstractFragility fracture of the sacrum has been an issue for elderly people. Research has been limited for the sacrum partly because its shape is complex with large inter-subject variation. Also, large-scale statistical analysis of its shape and density distribution has been limited mainly due to the computational load in establishment of the voxel correspondences, i.e., deformable registration.In this study, we employed a convolutional neural network (CNN)-based deformable registration algorithm in the analysis of the sacral bone. The algorithm we employed, VoxelMorph (Dalca et al. Med Image Anal 2019), is characterized as an unsupervised algorithm where no ground truth deformation field is required. The algorithm also allows to create a conditional deformable template, which is a volume image exhibiting smallest deformation field from all samples with specific attributes (e.g., age, sex, etc.), which in short represents the “average” (or “centroid”) image among the specific age and sex group. We applied it on a database consisting of 837 CTs (149 males, 688 females, 58.14 ± 14.73 y.o.) of the pelvis region, where the sacral bone was segmented and masked. We computed the templates corresponding to ages of 20 to 80 for male and female. The templates visually illustrated reduction of the bone density with aging in both male and female. The quantitative analysis showed that the average CT value over the sacrum region was reduced from 135.73 HU to 39.88 HU for 20 y.o. template to 80 y.o. template. Keyphrases: bone density, deep learning, deformable registration, statistical atlas generation In: Ferdinando Rodriguez Y Baena and Fabio Tatti (editors). CAOS 2020. The 20th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 4, pages 155--159
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