Automated Localization and Segmentation of Vertebrae in the Micro-CT Images of Rabbit Fetuses using 3D Convolutional Neural Networks

In the pharmaceutical industry, micro-CT images of Dutch-Belted rabbit fetuses have been used for the assessment of compound-induced skeletal abnormalities in developmental and reproductive toxicology (DART) studies. In the automated approach proposed to assess the morphology of each bone, localization and segmentation of each vertebral bone is a critical task. In this work, we are extending our previous work for the localization of cervical vertebrae to the entire spine following a multivariate regression framework based on a 3D convolutional neural network (CNN). We also introduce a multitasking 3D CNN for the segmentation of each vertebral bone, in which features at the most compact level are processed with two additional convolution layers with max pooling to generate features leading to a classification of whether the patch contains a complete vertebra or not. This multi-tasking mechanism allows us to ensure only complete pieces of vertebrae are segmented. Experimenting on 345 rabbit fetuses with 80/10/10 ratio for training/validation/testing, we were able to achieve successful localization on 94.3% of the cases (i.e. median bone-by-bone localization error under 5 voxels over the entire spine) and an average Dice similarity coefficient (DSC) of 0.80 between automated and ground truth segmentations on the testing set.