Multi-vertebrae Segmentation from Arbitrary Spine MR Images Under Global View

Multi-vertebrae segmentation plays an important role in spine diseases diagnosis and treatment planning. Global spatial dependencies between vertebrae are essential prior information for automatic multi-vertebrae segmentation. However, due to the lack of global information, previous methods have to localize specific vertebrae regions first, then segment and recognize the vertebrae in the region, resulting in a reduction in feature reuse and increase in computation. In this paper, we propose to leverage both global spatial and label information for multi-vertebrae segmentation from arbitrary MR images in one go. Specifically, a spatial graph convolutional network (GCN) is designed to first automatically learn an adjacency matrix and construct a graph on local feature maps, then adopt stacked GCN to capture the global spatial relationships between vertebrae. A label attention network is built to predict the appearance probabilities of all vertebrae using attention mechanism to reduce the ambiguity caused by variant FOV or similar appearances of adjacent vertebrae. The proposed method is trained in an end-to-end manner and evaluated on a challenging dataset of 292 MRI scans with various fields of view, image characteristics and vertebra deformations. The experimental results show that our method achieves high performance (\(89.28\pm 5.21\) of IDR and \(85.37\pm 4.09\%\) of mIoU) from arbitrary input images.