mlsanityData to 7 Oct 2026

Attention-based Shape-Deformation Networks for Artifact-Free Geometry Reconstruction of Lumbar Spine from MR Images

arXiv 2404.00231 ↗First posted 30 Mar 2024cs.CV cs.AI cs.LGcited by 3

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Abstract (arXiv)

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 textitUNet-DeformSA and textitTransDeformer: novel attention-based deep neural networks that reconstruct the geometry of the lumbar spine with high spatial accuracy and mesh correspondence across patients, and we also present a variant of textitTransDeformer for error estimation. Specially, we devise new attention modules with a new attention formula, which integrate 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 an image. Experiment results show that our networks generate artifact-free geometry outputs, and the variant of textitTransDeformer can predict the errors of a reconstructed geometry. Our code is available at https://github.com/linchenq/TransDeformer-Mesh.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 25 of its 53 references that are in the map. First posted on arXiv 30 Mar 2024.

Builds on

25 of 53 references.

References · 25

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13 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Shape Deformation Networks for Automated Aortic Valve Finite Element Meshing from 3D CT Imagescited by 0
  2. 2026SpineReport: Automated 3D Quantification and Reporting of Lumbar Spine Degeneration on MRIcited by 0
  3. 2026High-Fidelity 3D Geometric Reconstruction of Pelvic Organs from MRI: A Hybrid Deep Learning and Iterative Optimization Approachcited by 0