A New Angle on Bones: Robust Pose Estimation in X-Ray and Ultrasound

• URL: http://arxiv.org/abs/2606.04700v1
• Date: Wed, 03 Jun 2026 10:25:08 GMT
• Title: A New Angle on Bones: Robust Pose Estimation in X-Ray and Ultrasound
• Abstract summary: Measuring the angle between bone structures is a routine task in medical image analysis and provides a key quantitative parameter for diagnosis and treatment planning.<n>We address automatic bone pose estimation using a learning-based point candidate proposal followed by a line model to extract axis parameters.<n>We evaluate our method on three clinically relevant paediatric angle estimation tasks: fracture fragment assessment in radiographs and ultrasound and developmental dysplasia of the hip evaluation in ultrasound.
• Score: 5.592342871855084
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Measuring the angle between bone structures is a routine task in medical image analysis and provides a key quantitative parameter for diagnosis and treatment planning. Automated methods can reduce time and cost while improving reproducibility. In this work, we address automatic bone pose estimation using a learning-based point candidate proposal followed by a line model to extract axis parameters. Since conventional line models such as least squares are sensitive to outliers, we incorporate false-positive reduction strategies and robust fitting techniques, such as RANSAC and Hough transforms, to improve robustness. We evaluate our method on three clinically relevant paediatric angle estimation tasks: fracture fragment assessment in radiographs and ultrasound and developmental dysplasia of the hip evaluation in ultrasound using the Graf method. Our approach achieves mean errors of $4.1^\circ$, $5.4^\circ$, and $5.51^\circ$, respectively, not only remaining within the expected clinical observer variability, but also significantly outperforming landmark-based methods. Our code and annotations for fracture angle assessment in radiographs are publicly available on GitHub.

Related papers

• Rendering Novel Views of MRI Using 3D Gaussian Splatting [54.6286495315908]
  We adapt 3D Gaussian Splatting to form a reconstruction starting from sparse anisotropic MRIs, and imaging planes aligned with the anatomy relevant for clinical evaluation are then sampled and rendered.<n>The novel view plane is optimal for diagnostic radiological grading of the target anatomy, whereas the original MRI is not.
  arXiv  Detail & Related papers  (2026-06-24T18:00:03Z)
• Accurate Cobb Angle Estimation via SVD-Based Curve Detection and Vertebral Wedging Quantification [15.50536450875717]
  Adolescent idiopathic scoliosis (AIS) is a common spinal deformity affecting approximately 2.2% of boys and 4.8% of girls worldwide.<n>Traditional methods use simplified spinal models and predetermined curve patterns that fail to address clinical complexity.<n>We present a novel deep learning framework for AIS assessment that simultaneously predicts both superior and inferior endplate angles with corresponding midpoint coordinates for each vertebra.
  arXiv  Detail & Related papers  (2025-09-29T15:07:55Z)
• Periodontal Bone Loss Analysis via Keypoint Detection With Heuristic Post-Processing [1.918319133902462]
  This study proposes a deep learning framework and annotation methodology for the automatic detection of periodontal bone loss landmarks, associated conditions, and staging.<n>192 periapical domains were collected and annotated with a stage PRC methodology, labelling relevant landmarks regardless of disease presence or extent.<n>We propose a post-processing module that aligns keypoints to tooth boundaries using an auxiliary instance segmentation model.
  arXiv  Detail & Related papers  (2025-03-05T00:34:29Z)
• Intuitive Axial Augmentation Using Polar-Sine-Based Piecewise Distortion for Medical Slice-Wise Segmentation [4.471795611968146]
  We revisit and acknowledge the unique characteristics of medical images apart from traditional digital images.<n>We propose a medical-specific augmentation algorithm that is more elastic and aligns well with radiology scan procedure.<n>Our method is highlighted for its intuitive design and ease of understanding for medical professionals.
  arXiv  Detail & Related papers  (2024-12-04T14:35:06Z)
• Automatic Ultrasound Curve Angle Measurement via Affinity Clustering for Adolescent Idiopathic Scoliosis Evaluation [1.9747854071595796]
  The current clinical gold standard for evaluating adolescent idiopathic scoliosis (AIS) is X-ray radiography, using Cobb angle measurement. We introduce an estimation model for automatic ultrasound curve angle (UCA) measurement. The model employs a dual-branch network to detect candidate landmarks and perform vertebra segmentation on ultrasound coronal images.
  arXiv  Detail & Related papers  (2024-05-06T03:28:47Z)
• B-Spine: Learning B-Spline Curve Representation for Robust and Interpretable Spinal Curvature Estimation [50.208310028625284]
  We propose B-Spine, a novel deep learning pipeline to learn B-spline curve representation of the spine. We estimate the Cobb angles for spinal curvature estimation from low-quality X-ray images.
  arXiv  Detail & Related papers  (2023-10-14T15:34:57Z)
• Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders [50.689585476660554]
  We propose a new fine-tuning strategy that includes positive-pair loss relaxation and random sentence sampling. Our approach consistently improves overall zero-shot pathology classification across four chest X-ray datasets and three pre-trained models.
  arXiv  Detail & Related papers  (2022-12-14T06:04:18Z)
• Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations [11.415942647070796]
  We show how manual annotations routinely collected in a clinic can be efficiently used to ease this task. Ill-posed but repetitive imaging tasks can be eased or automated by leveraging imperfect, retrospective clinical annotations.
  arXiv  Detail & Related papers  (2021-11-22T15:11:36Z)
• Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application [48.89674088331313]
  We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow.
  arXiv  Detail & Related papers  (2021-08-21T16:15:40Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Vertebra-Focused Landmark Detection for Scoliosis Assessment [54.24477530836629]
  We propose a novel vertebra-focused landmark detection method. Our model first localizes the vertebra centers, based on which it then traces the four corner landmarks of the vertebra through the learned corner offset. Results demonstrate the merits of our method in both Cobb angle measurement and landmark detection on low-contrast and ambiguous X-ray images.
  arXiv  Detail & Related papers  (2020-01-09T19:17:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.