  This paper proposes a novel transformer-based model architecture for medical
imaging problems involving analysis of vertebrae. It considers two applications
of such models in MR images: (a) detection of spinal metastases and the related
conditions of vertebral fractures and metastatic cord compression, (b)
radiological grading of common degenerative changes in intervertebral discs.
Our contributions are as follows: (i) We propose a Spinal Context Transformer
(SCT), a deep-learning architecture suited for the analysis of repeated
anatomical structures in medical imaging such as vertebral bodies (VBs). Unlike
previous related methods, SCT considers all VBs as viewed in all available
image modalities together, making predictions for each based on context from
the rest of the spinal column and all available imaging modalities. (ii) We
apply the architecture to a novel and important task: detecting spinal
metastases and the related conditions of cord compression and vertebral
fractures/collapse from multi-series spinal MR scans. This is done using
annotations extracted from free-text radiological reports as opposed to bespoke
annotation. However, the resulting model shows strong agreement with
vertebral-level bespoke radiologist annotations on the test set. (iii) We also
apply SCT to an existing problem: radiological grading of inter-vertebral discs
(IVDs) in lumbar MR scans for common degenerative changes.We show that by
considering the context of vertebral bodies in the image, SCT improves the
accuracy for several gradings compared to previously published model.
