The vertebral levels of the spine provide a useful coordinate system when making measurements of plaque, muscle, fat, and bone mineral density. Correctly classifying vertebral levels with high accuracy is challenging due to the similar appearance of each vertebra, the curvature of the spine, and the possibility of anomalies such as fractured vertebrae, implants, lumbarization of the sacrum, and sacralization of L5. The goal of this work is to develop a system that can accurately and robustly identify the L1 level in large heterogeneous datasets. The first approach we study is using a 3D U-Net to segment the L1 vertebra directly using the entire scan volume to provide context. We also tested models for two class segmentation of L1 and T12 and a three class segmentation of L1, T12 and the rib attached to T12. By increasing the number of training examples to 249 scans using pseudo-segmentations from an in-house segmentation tool we were able to achieve 98% accuracy with respect to identifying the L1 vertebra, with an average error of 4.5 mm in the craniocaudal level. We next developed an algorithm which performs iterative instance segmentation and classification of the entire spine with a 3D U-Net. We found the instance based approach was able to yield better segmentations of nearly the entire spine, but had lower classification accuracy for L1.
A persistent issue in deep learning (DL) is the inability of models to function in a domain in which they were not trained. For example, a model trained to segment an organ in MRI scans often dramatically fails when tested in the domain of computed tomography (CT) scans. Since manual segmentation is extremely timeconsuming, it is often not feasible to acquire an annotated dataset in the target domain. Domain adaptation allows transfer of knowledge about a labelled source domain into a target domain. In this work, we attempt to address the differences in model performance when segmenting from intravenous contrast (IVC) enhanced or from non-contrast (NC) CT scans. Most of the publicly available, large-scale, annotated CT datasets are IVCenhanced. However, physicians frequently use NC scans in clinical practice. This necessitates methods capable of reliably functioning across both domains. We propose a novel DL framework that can segment the pancreas from non-contrast CT scans through training with the help of IVC-enhanced CT scans. Our method first utilizes a CycleGAN to create synthetic NC (s-NC) variants from IVC scans. Subsequently, we introduce a multilevel 3D UNet architecture to perform pancreas segmentation. The proposed method significantly outperforms the baseline. Experimental results show 6.2% percent improvement compared to the baseline model in terms of the Dice coefficient. To our knowledge, this method is the first of its kind in pancreas segmentation from NC CTs.