A General Framework for Localizing and Locally Segmenting Correlated Objects: A Case Study on Intervertebral Discs in Multi-modality MR Images

Low back pain is a leading cause of disability that has been associated with intervertebral disc (IVD) degeneration by various clinical studies. With MRT being the imaging technique of choice for IVDs due to its excellent soft tissue contrast, we propose a fully automatic approach for localizing and locally segmenting spatially correlated objects—tailored to cope with a limited set of training data while making very few domain assumptions—and apply it to lumbar IVDs in multi-modality MR images. Regression tree ensembles spatially regularized by a conditional random field are used to find the IVD centroids, which allows to cut fixed-size sections around each IVD to efficiently perform the segmentation on the sub-volumes. Exploiting the similar imaging characteristics of IVD tissue, we build an IVD-agnostic V-Net to perform the segmentation and train it on all IVDs (instead of a specific one). In particular, we compare the usage of binary (i.e., pairwise) CRF potentials combined with a latent scaling variable to tackle spine size variability with scaling-invariant ternary potentials. Evaluating our approach on a public challenge data set consisting of 16 cases from 8 subjects with 4 modalities each, we achieve an average Dice coefficient of 0.904, an average absolute surface distance of 0.423 mm and an average center distance of 0.59 mm.