arXiv cs.AI· Augustin Daridon, Sofiane Sifaoui, Ayman Assi, Mohamad Karam, Wafa Skalli, Elsa Angelini, Loic Le Folgoc·· 4 小时前AI 评分22
脊柱关键点检测的稠密表示学习与可微形状建模方法
Dense Representation Learning and Differentiable Shape Modeling for Spine Landmark Detection
AI 导读
一项研究将脊柱 X 光关键点检测重新表述为像素级对比表示学习问题，用稠密像素嵌入匹配关键点原型，即使使用通用 U-Net 骨干也能生成比传统热图回归更具判别性的热图。方法进一步结合可学习的形状先验来解决关键点位置歧义，在公开 AASCE 数据集及两个私有脊柱 X 光数据集上达到 SOTA 水平的关键点精度与临床参数估计，代码和预训练权重已开源。
正文 · 原文
Abstract:Spine landmark detection from spinal X-ray is a key step for estimating spine deformation severity, for building biomechanical models, and for pre-operative planning. We reframe anatomical landmark detection as a pixel-level contrastive representation learning problem, matching dense pixel embeddings with landmark prototypes. Surprisingly, even with a generic U-Net backbone, this formulation results in more discriminative heatmaps compared to the traditional heatmap regression paradigm, leading to improved discrimination between vertebral corners on distinct vertebra. Furthermore, we combine the resulting heatmaps with learnable shape priors over the global configuration of spinal landmarks to resolve ambiguities in landmark locations, yielding a simple and practical approach to spinal landmark detection. Our approach is competitive, in terms of landmark accuracy and clinical parameter estimation, with the state-of-the-art on the public AASCE dataset and on two private spinal X-ray datasets. Our code and pre-trained weights are available at this https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.36951 [cs.CV] |
| (or arXiv:2610.36951v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36951 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sofiane Sifaoui [view email]
[v1]
Tue, 27 Oct 2026 10:11:18 UTC (7,726 KB)
来源：arXiv cs.AI · arxiv.org