Fully automatic cross-modality localization and labeling of vertebral bodies and intervertebral discs in 3D spinal images.

PURPOSE: We present a cross-modality and fully automatic pipeline for labeling of intervertebral discs and vertebrae in volumetric data of the lumbar and thoracolumbar spine. The main goal is to provide an algorithm that is applicable to a wide range of different sequences and acquisition protocols, like T1- and T2- weighted MR scans, MR Dixon data, and CT scans. This requires that the learned models generalize without retraining to modalities and scans with unseen image contrasts.
METHODS: We address this challenge by automatically localizing the sacral region combining local entropy-optimized texture models with convolutional neural networks. For subsequent labeling, local three-disc entropy models are matched iteratively to the spinal column. Every model-matched position is further refined by an intensity-based template-matching approach, based solely on the reduced intensity scale provided by the entropy models.
RESULTS: We evaluated our method on 161 publicly available scans, acquired on various scanners. We showed that our method can deal with a wide range of different MR protocols as well as with CT data. We achieved a sacrum detection rate of 93.6%. Mean center accuracies ranged from 2.5 ± 1.5 to 5.7 ± 3.8 mm for the different sets of scans.
CONCLUSION: We present a novel spine labeling framework that is applicable to a highly heterogeneous set of scans without retraining of the method. Our approach achieves high sacrum localization accuracy and shows promising labeling results. To the best of our knowledge, an algorithm able to deal with such a diverse set of MR and CT scans has not yet been presented in the literature.