CurvNet: Latent Contour Representation and Iterative Data Engine for Curvature Angle Estimation

• URL: http://arxiv.org/abs/2411.12604v2
• Date: Thu, 09 Oct 2025 10:25:48 GMT
• Title: CurvNet: Latent Contour Representation and Iterative Data Engine for Curvature Angle Estimation
• Authors: Zhiwen Shao, Yichen Yuan, Lizhuang Ma, Xiaojia Zhu,
• Abstract summary: CurvNet is a curvature angle estimation framework.<n>We develop a data engine with image self-generation, automatic annotation, and automatic selection.<n>Our method achieves state-of-the-art Cobb angle estimation performance.
• Score: 50.71992730453576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Curvature angle is a quantitative measurement of a curve, in which Cobb angle is customized for spinal curvature. Automatic Cobb angle measurement from X-ray images is crucial for scoliosis screening and diagnosis. However, most existing regression-based and segmentation-based methods struggle with inaccurate spine representations or mask connectivity and fragmentation issues. Besides, landmark-based methods suffer from insufficient training data and annotations. To address these challenges, we propose a novel curvature angle estimation framework named CurvNet including latent contour representation based contour detection and iterative data engine based image self-generation. Specifically, we propose a parameterized spine contour representation in latent space, which enables eigen-spine decomposition and spine contour reconstruction. Latent contour coefficient regression is combined with anchor box classification to solve inaccurate predictions and mask connectivity issues. Moreover, we develop a data engine with image self-generation, automatic annotation, and automatic selection in an iterative manner. By our data engine, we generate a clean dataset named Spinal-AI2024 without privacy leaks, which is the largest released scoliosis X-ray dataset to our knowledge. Extensive experiments on public AASCE2019, our private Spinal2023, and our generated Spinal-AI2024 datasets demonstrate that our method achieves state-of-the-art Cobb angle estimation performance. Our code and Spinal-AI2024 dataset are available at https://github.com/Ernestchenchen/CurvNet and https://github.com/Ernestchenchen/Spinal-AI2024, respectively.

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