Abstract
Lumbar disc degeneration, a progressive structural wear and tear of lumbar
intervertebral disc, is regarded as an essential role on low back pain, a
significant global health concern. Automated lumbar spine geometry
reconstruction from MR images will enable fast measurement of medical
parameters to evaluate the lumbar status, in order to determine a suitable
treatment. Existing image segmentation-based techniques often generate
erroneous segments or unstructured point clouds, unsuitable for medical
parameter measurement. In this work, we present TransDeformer: a novel
attention-based deep learning approach that reconstructs the contours of the
lumbar spine with high spatial accuracy and mesh correspondence across
patients, and we also present a variant of TransDeformer for error estimation.
Specially, we devise new attention modules with a new attention formula, which
integrates image features and tokenized contour features to predict the
displacements of the points on a shape template without the need for image
segmentation. The deformed template reveals the lumbar spine geometry in the
input image. We develop a multi-stage training strategy to enhance model
robustness with respect to template initialization. Experiment results show
that our TransDeformer generates artifact-free geometry outputs, and its
variant predicts the error of a reconstructed geometry. Our code is available
at https://github.com/linchenq/TransDeformer-Mesh.