Segmentation of vertebrae and intervertebral discs in lumbar spine MR images with iterative instance segmentation

Segmentation of vertebrae and intervertebral discs (IVD) in MR images are important steps for automatic image analysis. This paper proposes an extension of an iterative vertebra segmentation method that relies on a 3D fully-convolutional neural network to segment the vertebrae one-by-one. We augment this approach with an additional segmentation step following each vertebra detection to also segment the IVD below each vertebra. To train and test the algorithm, we collected and annotated T2-weighted sagittal lumbar spine MR scans of 53 patients. The presented approach achieved a mean Dice score of 93 % +- 2 % for vertebra segmentation and 86 % +- 7 % for IVD segmentation. The method was able to cope with pathological abnormalities such as compression fractures, Schmorl's nodes and collapsed IVDs. In comparison, a similar network trained for IVD segmentation without knowledge of the adjacent vertebra segmentation result did not detect all IVDs (89 %) and also achieved a lower Dice score of 83 % +- 9 %. These results indicate that combining IVD segmentation with vertebra segmentation in lumbar spine MR images can help to improve the detection and segmentation performance compared with separately segmenting these structures.