mlsanityData to 7 Oct 2026

Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography

arXiv 2307.11513 ↗First posted 21 Jul 2023eess.IV cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

Osteoporosis is a prevalent bone disease that causes fractures in fragile bones, leading to a decline in daily living activities. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) are highly accurate for diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. To frequently monitor bone health, low-cost, low-dose, and ubiquitously available diagnostic methods are highly anticipated. In this study, we aim to perform bone mineral density (BMD) estimation from a plain X-ray image for opportunistic screening, which is potentially useful for early diagnosis. Existing methods have used multi-stage approaches consisting of extraction of the region of interest and simple regression to estimate BMD, which require a large amount of training data. Therefore, we propose an efficient method that learns decomposition into projections of bone-segmented QCT for BMD estimation under limited datasets. The proposed method achieved high accuracy in BMD estimation, where Pearson correlation coefficients of 0.880 and 0.920 were observed for DXA-measured BMD and QCT-measured BMD estimation tasks, respectively, and the root mean square of the coefficient of variation values were 3.27 to 3.79% for four measurements with different poses. Furthermore, we conducted extensive validation experiments, including multi-pose, uncalibrated-CT, and compression experiments toward actual application in routine clinical practice.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 24 of its 77 references that are in the map. First posted on arXiv 21 Jul 2023.

Builds on

24 of 77 references.

References · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  3. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  4. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  5. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,417
  6. 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,176
  7. 2021CoAtNet: Marrying Convolution and Attention for All Data Sizescited by 566
  8. 2020Differentiable Augmentation for Data-Efficient GAN Trainingcited by 361
  9. 2021DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentationcited by 299
  10. 2019Large Scale Adversarial Representation Learningcited by 238
  11. 2019Full-Gradient Representation for Neural Network Visualizationcited by 189
  12. 2019Consistency Regularization for Generative Adversarial Networkscited by 134
12 more
  1. 2021Targeted Supervised Contrastive Learning for Long-Tailed Recognitioncited by 132
  2. 2017Representation Learning by Learning to Countcited by 120
  3. 2022Long-Tailed Recognition via Weight Balancingcited by 107
  4. 2021Regularizing Generative Adversarial Networks under Limited Datacited by 105
  5. 2018Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data sizecited by 42
  6. 2021Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalitiescited by 42
  7. 2021Gradient Normalization for Generative Adversarial Networkscited by 25
  8. 2019Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal Modelingcited by 22
  9. 2019Image to Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiographycited by 10
  10. 2022Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modelingcited by 4
  11. 2020Automated segmentation of an intensity calibration phantom in clinical CT images using a convolutional neural networkcited by 3
  12. 2022BMD-GAN: Bone mineral density estimation using x-ray image decomposition into projections of bone-segmented quantitative computed tomography using hierarchical learningcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.

  1. 20243DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth Estimationcited by 2
  2. 2024Enhancing Quantitative Image Synthesis through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-ray Imagecited by 0
  3. 2026RFS-UNet: Decoder-Conditioned High-Resolution Skip Recalibration for Bone-Selective DRR Synthesiscited by 0