Latent3DU-net: Multi-level Latent Shape Space Constrained 3D U-net for Automatic Segmentation of the Proximal Femur from Radial MRI of the Hip
Radial 2D MRI scans of the hip are routinely used for the diagnosis of the cam-type of femoroacetabular impingement (FAI) and of avascular necrosis (AVN) of the femoral head, which are considered causes of hip joint osteoarthritis in young and active patients. However, for computer assisted planning of surgical treatment, it is highly desired to have 3D models of the proximal femur. In this paper, we propose a novel volumetric convolutional neural network (CNN) based framework to fully automatically extract 3D models of the proximal femur from sparsely hip radial slices. Our framework starts with a spatial transform to interpolate sparse 2D radial MR images to a densely sampled 3D volume data. Automated segmentation of the interpolated 3D volume data is very challenging due to the poor image quality and the interpolation artifact. To tackle these challenges, we introduce a multi-level latent shape space constrained 3D U-net, referred as Latent3DU-net, to incorporate prior shape knowledge into voxelwise semantic segmentation of the interpolated 3D volume. Comprehensive results obtained from 25 patient data demonstrated the effectiveness of the proposed framework.
This study was partially supported by the Swiss National Science Foundation via project 205321_163224/1.
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