Dual-Energy X-Ray Absorptiometry for Measurement of Phalangeal Bone Mineral Density on a Slot-Scanning Digital Radiography System

Dendere, Ronald; Potgieter, J.H.; Steiner, Stefan H.; Whiley, S. P.; Douglas, Tania S.

doi:10.1109/tbme.2015.2447575

Cited by 12 publications

(5 citation statements)

References 47 publications

(82 reference statements)

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“…Segmentation errors can significantly impact the BMD calculation and subsequent analyses. However, several reasons drive incorrect segmentation in DXA images and post-analysis [4,5]. Firstly, the use of low-dose X-rays in DXA imaging provides noisy images.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Impact of Noise and Image Quality on Deep Learning Performance in DXA Images

Hussain,

Hyeon Gu

2024

Diagnostics

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Background and Objective: Segmentation of the femur in Dual-Energy X-ray (DXA) images poses challenges due to reduced contrast, noise, bone shape variations, and inconsistent X-ray beam penetration. In this study, we investigate the relationship between noise and certain deep learning (DL) techniques for semantic segmentation of the femur to enhance segmentation and bone mineral density (BMD) accuracy by incorporating noise reduction methods into DL models. Methods: Convolutional neural network (CNN)-based models were employed to segment femurs in DXA images and evaluate the effects of noise reduction filters on segmentation accuracy and their effect on BMD calculation. Various noise reduction techniques were integrated into DL-based models to enhance image quality before training. We assessed the performance of the fully convolutional neural network (FCNN) in comparison to noise reduction algorithms and manual segmentation methods. Results: Our study demonstrated that the FCNN outperformed noise reduction algorithms in enhancing segmentation accuracy and enabling precise calculation of BMD. The FCNN-based segmentation approach achieved a segmentation accuracy of 98.84% and a correlation coefficient of 0.9928 for BMD measurements, indicating its effectiveness in the clinical diagnosis of osteoporosis. Conclusions: In conclusion, integrating noise reduction techniques into DL-based models significantly improves femur segmentation accuracy in DXA images. The FCNN model, in particular, shows promising results in enhancing BMD calculation and clinical diagnosis of osteoporosis. These findings highlight the potential of DL techniques in addressing segmentation challenges and improving diagnostic accuracy in medical imaging.

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Section: Introductionmentioning

confidence: 99%

Exploring the Impact of Noise and Image Quality on Deep Learning Performance in DXA Images

Hussain,

Hyeon Gu

2024

Diagnostics

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“…All of these factors lead to significant reduction in bone structure [9]- [11]. In order to diagnose bone, whether it is infected or not, we need to measure bone mineral density [12], [13], there are several techniques to measure bone mineral density, such as X-ray [14], CT scan [15], dual energy X-ray absorptiometry (DEXA) [16] scan which differ in their quality and radiation dose. We will use DEXA because it has lower radiation dose than CT scan [17].…”

Section: Introductionmentioning

confidence: 99%

Osteoporosis detection using convolutional neural network based on dual-energy X-ray absorptiometry images

Mohammed

George

2022

IJEECS

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Osteoporosis is one of the most common diseases that affect the bones of adults, especially women in menopause, and the reason for this is due to the lack of bone mineral density bone mineral density (BMD). BMD can be measured by X-ray and dual energy X-ray absorptiometry (DEXA) images, this article, focused on using DEXA images for Osteoporosis detection. At first, the original image must passed through the preprocessing stage, during which the noisy parts is reduced, and the useless parts are eliminated, and then the contrast between adjacent areas is increased and the area of interest is allocated. After that, the image is passed in a deep learning model in order to extract the unique features on the basis of which each image is classified. The classification result was excellent with 98% accuracy. The used dataset is “Osteoporosis DEXA scans images” of Spine from Pakistan.

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“…More than 20% of people die from fractures that occur from osteoporosis [1]. The medical imaging technology known as dual-energy X-ray absorptiometry (DXA) can adequately diagnose this disease and is currently considered the gold standard for such diagnoses [2][3][4]. Meanwhile, quantitative computed tomography exists as an alternative method, but it demands a large dose of X-rays and is overpriced.…”

Section: Introductionmentioning

confidence: 99%

“…Careful DXA image analysis and precise BMD calculation support the pinpoint segmentation of soft tissue and bone. On the other hand, imprecise bone and soft tissue segmentation severely affect BMD calculation and subsequent analysis of DXA image [3,4]. Unfortunately, erroneous bone region identification is a widespread problem in femur DXA images for several reasons.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in DXA Image Segmentation

Hussain¹,

Loh²,

Lee³

2021

Computers, Materials &Amp; Continua

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Many existing techniques to acquire dual-energy X-ray absorptiometry (DXA) images are unable to accurately distinguish between bone and soft tissue. For the most part, this failure stems from bone shape variability, noise and low contrast in DXA images, inconsistent X-ray beam penetration producing shadowing effects, and person-to-person variations. This work explores the feasibility of using state-of-the-art deep learning semantic segmentation models, fully convolutional networks (FCNs), SegNet, and U-Net to distinguish femur bone from soft tissue. We investigated the performance of deep learning algorithms with reference to some of our previously applied conventional image segmentation techniques (i.e., a decision-tree-based method using a pixel label decision tree [PLDT] and another method using Otsu's thresholding) for femur DXA images, and we measured accuracy based on the average Jaccard index, sensitivity, and specificity. Deep learning models using SegNet, U-Net, and an FCN achieved average segmentation accuracies of 95.8%, 95.1%, and 97.6%, respectively, compared to PLDT (91.4%) and Otsu's thresholding (72.6%). Thus we conclude that an FCN outperforms other deep learning and conventional techniques when segmenting femur bone from soft tissue in DXA images. Accurate femur segmentation improves bone mineral density computation, which in turn enhances the diagnosing of osteoporosis.

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Dual-Energy X-Ray Absorptiometry for Measurement of Phalangeal Bone Mineral Density on a Slot-Scanning Digital Radiography System

Abstract: We have shown the clinical potential of phalangeal DXA measurements of aBMD and vBMD on a slot-scanning digital radiography system.

Cited by 12 publications

References 47 publications

Exploring the Impact of Noise and Image Quality on Deep Learning Performance in DXA Images

Exploring the Impact of Noise and Image Quality on Deep Learning Performance in DXA Images

Osteoporosis detection using convolutional neural network based on dual-energy X-ray absorptiometry images

Deep Learning in DXA Image Segmentation

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