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 this https URL.
Abstract (translated)
腰椎间盘退化，腰椎间歇性椎间盘的渐进性结构和功能退化，被认为是导致腰痛和全球健康问题的关键因素。通过从MRI图像中自动重建腰椎脊柱几何形状，将能够快速测量医疗参数以评估腰椎状况，从而确定合适的治疗方案。现有的图像分割为基础的方法通常产生错误的分割或无结构的点云，不适合医疗参数测量。在这项工作中，我们提出了TransDeformer：一种基于注意力的深度学习方法，具有高空间精度和高网格对应性，用于重建腰椎脊柱的轮廓。我们还介绍了一种TransDeformer用于错误估计的变体。特别，我们设计了一种新的注意力模块，采用新的注意力公式，将图像特征和分割轮廓特征集成在一起，预测形状模板上点的位移，无需进行图像分割。变形模板揭示了输入图像中的腰椎脊柱几何形状。我们开发了一种多阶段训练策略，以增强模型在模板初始化方面的鲁棒性。实验结果表明，我们的TransDeformer生成了无伪影的拓扑结构输出，而其变体预测了重构几何的误差。我们的代码可在此处访问：https://www.thisurl.com/
URL
https://arxiv.org/abs/2404.00231
