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

arXiv 2411.12604 ↗First posted 19 Nov 2024cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 42 references that are in the map. First posted on arXiv 19 Nov 2024.

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21 of 42 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2023Segment Anythingcited by 8,158
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  6. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
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  8. 2020Training Generative Adversarial Networks with Limited Datacited by 1,184
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  10. 2021In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learningcited by 301
  11. 2019Probabilistic End-to-end Noise Correction for Learning with Noisy Labelscited by 210
  12. 2016Resnet in Resnet: Generalizing Residual Architecturescited by 110
9 more
  1. 2021Graph Stacked Hourglass Networks for 3D Human Pose Estimationcited by 94
  2. 2021From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimationcited by 48
  3. 2022Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systemscited by 44
  4. 2017Improving Landmark Localization with Semi-Supervised Learningcited by 39
  5. 2019Repetitive Reprediction Deep Decipher for Semi-Supervised Learningcited by 11
  6. 2020Vertebra-Focused Landmark Detection for Scoliosis Assessmentcited by 11
  7. 2019Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regressioncited by 8
  8. 2023LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Networkcited by 8
  9. 2022Automating Cobb Angle Measurement for Adolescent Idiopathic Scoliosis using Instance Segmentationcited by 1

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