Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modeling

• URL: http://arxiv.org/abs/2201.01838v1
• Date: Wed, 5 Jan 2022 22:03:32 GMT
• Title: Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modeling
• Authors: Fakai Wang, Kang Zheng, Le Lu, Jing Xiao, Min Wu, Chang-Fu Kuo and Shun Miao
• Abstract summary: Osteoporosis is a chronic metabolic bone disease that is often under-diagnosed and under-treated due to the limited access to bone mineral density examinations. In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most commonly accessible and low-cost medical imaging examinations.
• Score: 23.014342480592873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Osteoporosis is a common chronic metabolic bone disease that is often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g. via Dual-energy X-ray Absorptiometry (DXA). In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most commonly accessible and low-cost medical imaging examinations. Our method first automatically detects Regions of Interest (ROIs) of local and global bone structures from the CXR. Then a multi-ROI deep model with transformer encoder is developed to exploit both local and global information in the chest X-ray image for accurate BMD estimation. Our method is evaluated on 13719 CXR patient cases with their ground truth BMD scores measured by gold-standard DXA. The model predicted BMD has a strong correlation with the ground truth (Pearson correlation coefficient 0.889 on lumbar 1). When applied for osteoporosis screening, it achieves a high classification performance (AUC 0.963 on lumbar 1). As the first effort in the field using CXR scans to predict the BMD, the proposed algorithm holds strong potential in early osteoporosis screening and public health promotion.

Related papers

• Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography [4.872603360039571]
  Osteoporosis is a prevalent bone disease that causes fractures in fragile bones, leading to a decline in daily living activities. 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 estimation from a plain X-ray image for opportunistic screening.
  arXiv  Detail & Related papers  (2023-07-21T11:49:30Z)
• Revisiting Computer-Aided Tuberculosis Diagnosis [56.80999479735375]
  Tuberculosis (TB) is a major global health threat, causing millions of deaths annually. Computer-aided tuberculosis diagnosis (CTD) using deep learning has shown promise, but progress is hindered by limited training data. We establish a large-scale dataset, namely the Tuberculosis X-ray (TBX11K) dataset, which contains 11,200 chest X-ray (CXR) images with corresponding bounding box annotations for TB areas. This dataset enables the training of sophisticated detectors for high-quality CTD.
  arXiv  Detail & Related papers  (2023-07-06T08:27:48Z)
• Opportunistic hip fracture risk prediction in Men from X-ray: Findings from the Osteoporosis in Men (MrOS) Study [0.7340017786387765]
  Osteoporosis is a common disease that increases fracture risk. Hip fractures, especially in elderly people, lead to increased morbidity, decreased quality of life and increased mortality. Being a silent disease before fracture, osteoporosis often remains undiagnosed and untreated.
  arXiv  Detail & Related papers  (2022-07-22T09:35:48Z)
• BMD-GAN: Bone mineral density estimation using x-ray image decomposition into projections of bone-segmented quantitative computed tomography using hierarchical learning [1.8762753243053634]
  We propose an approach using the QCT for training a generative adversarial network (GAN) and decomposing an x-ray image into a projection of bone-segmented QCT. The evaluation of 200 patients with osteoarthritis using the proposed method demonstrated a Pearson correlation coefficient of 0.888 between the predicted and ground truth.
  arXiv  Detail & Related papers  (2022-07-07T10:33:12Z)
• Fast and Robust Femur Segmentation from Computed Tomography Images for Patient-Specific Hip Fracture Risk Screening [48.46841573872642]
  We propose a deep neural network for fully automated, accurate, and fast segmentation of the proximal femur from CT. Our method is apt for hip-fracture risk screening, bringing us one step closer to a clinically viable option for screening at-risk patients for hip-fracture susceptibility.
  arXiv  Detail & Related papers  (2022-04-20T16:16:16Z)
• Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Auto-Encoders [65.95959936242993]
  We develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone lesions. It will assist in planning and evaluating treatment strategies to prevent skeletal related events (SREs) in breast cancer patients.
  arXiv  Detail & Related papers  (2022-03-20T21:00:10Z)
• Opportunistic Screening of Osteoporosis Using Plain Film Chest X-ray [23.41545684473636]
  Osteoporosis is a chronic metabolic bone disease that is often under-diagnosed and under-treated due to the limited access to bone mineral density examinations. In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most common, accessible, and low-cost medical image examinations.
  arXiv  Detail & Related papers  (2021-04-05T01:25:23Z)
• Semi-Supervised Learning for Bone Mineral Density Estimation in Hip X-ray Images [19.17169803995019]
  Bone mineral density is a clinically critical indicator of osteoporosis. Due to the limited accessibility of DEXA machines and examinations, osteoporosis is often under-diagnosed and under-treated.
  arXiv  Detail & Related papers  (2021-03-24T20:59:54Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI [70.04389979779195]
  We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs. This involves using a learnt vector field to group detected vertebrae corners together into individual vertebral bodies. We demonstrate the clinical applicability of this method, using it for automated scoliosis detection in both lumbar and whole spine MR scans.
  arXiv  Detail & Related papers  (2020-07-06T09:37:12Z)
• Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
  We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
  arXiv  Detail & Related papers  (2020-05-24T23:13:16Z)

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.