Localization and labeling of cervical vertebral bones in the micro-CT images of rabbit fetuses using a 3D deep convolutional neural network.

In developmental and reproductive toxicology (DART) studies, high-throughput micro-CT imaging of Dutch-Belted rabbit fetuses has been used as a method for the assessment of compound-induced skeletal abnormalities. Since performing visual inspection of the micro-CT images by the DART scientists is a time- and resource-intensive task, an automatic strategy was proposed to localize, segment out, label, and evaluate each bone on the skeleton in a testing environment. However, due to the lack of robustness in this bone localization approach, failures on localizing certain bones on the critical path while traversing the skeleton, e.g., the cervical vertebral bones, could lead to localization errors for other bones downstream. Herein an approach based on deep convolutional neural networks (CNN) is proposed to automatically localize each cervical vertebral bone represented by its center. For each center, a 3D probability map with Gaussian decay is computed with the center itself being the maximum. From cervical vertebrae Cl to C7, the 7 volumes of distance transforms are stacked in order to form a 4-dimensional array. The deep CNN with a 3D U-Net architecture is used to estimate the probability maps for vertebral bone centers from the CT images as the input. A post-processing scheme is then applied to find all the regions with positive response, eliminate the false ones using a point-based registration method, and provide the locations and labels for the 7 cervical vertebral bones. Experiments were carried out on a dataset of 345 rabbit fetus micro-CT volumes. The images were randomly divided into training/validation/testing sets at an 80/10/10 ratio. Results demonstrated a 94.3% success rate for localization and labeling on the testing dataset of 35 images, and for all the successful cases the average bone-by-bone localization error was at 0.84 voxel.