Automatic Whole-body Bone Age Assessment Using Deep Hierarchical Features

Nguyen, Hai-Duong; Kim, Soo-Hyung

doi:10.48550/arxiv.1901.10237

Cited by 2 publications

(3 citation statements)

References 13 publications

(14 reference statements)

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“…Age can also be determined from whole-body CT scans. In [12], Nguyen et al presented a solution based on deep hierarchical features. They used data from an anonymous hospital that contained 813 whole-body CT bone images.…”

Section: Introductionmentioning

confidence: 99%

Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

et al. 2022

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Age prediction from X-rays is an interesting research topic important for clinical applications such as biological maturity assessment. It is also useful in many other practical applications, including sports or forensic investigations for age verification purposes. Research on these issues is usually carried out using high-resolution X-ray scans of parts of the body, such as images of the hands or images of the chest. In this study, we used low-resolution, dual-energy, full-body X-ray absorptiometry images to train deep learning models to predict age. In particular, we proposed a preprocessing framework and adapted many partially pretrained convolutional neural network (CNN) models to predict the age of children and young adults. We used a new dataset of 910 multispectral images that were weakly annotated by specialists. The experimental results showed that the proposed preprocessing techniques and the adapted approach to the CNN model achieved a discrepancy between chronological age and predicted age of around 15.56 months for low-resolution whole-body X-rays. Furthermore, we found that the main factor that influenced age prediction scores was spatial features, not multispectral features.

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Section: Introductionmentioning

confidence: 99%

Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

et al. 2022

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“…Since a bone age assessment is of great importance, this topic has been addressed in order to support physicians with an automated analysis of the data, making this task less labor intensive. In the literature, there are many approaches to address this problem, when analyzing X-ray images of hands [39][40][41][42][43][44][45], the chest [40,46], or whole-body images [47,48]. In the case of a fully automated deep learning approach, first the hand region was determined in the image using the U-Net network for semantic segmentation of the hand region, then the image registration was applied to allow for an easy determination of hand regions corresponding to each other between various images.…”

Section: Introductionmentioning

confidence: 99%

“…A similar pipeline was introduced in previous studies [40,44,45], yet the authors underlined the importance of transfer learning when preparing regression models. There were also approaches that used one network for the evaluation of bone age, as presented in the research in which whole-body scans were analyzed using well-known deep architectures, such as VGGNet, GoogLeNet, and ResNet, to find the best solution [47], or the hand X-ray image was analyzed with the attention-Xception network [43]. In [46] not only was the age determined from the chest radiograph images, but also, they analyzed the activation maps to find the most characteristic regions that influence the patient's age.…”

Section: Introductionmentioning

confidence: 99%

Texture Analysis for the Bone Age Assessment from MRI Images of Adolescent Wrists in Boys

Obuchowicz

Nurzynska

Pierzchała³

et al. 2023

JCM

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Currently, bone age is assessed by X-rays. It enables the evaluation of the child’s development and is an important diagnostic factor. However, it is not sufficient to diagnose a specific disease because the diagnoses and prognoses may arise depending on how much the given case differs from the norms of bone age. Background: The use of magnetic resonance images (MRI) to assess the age of the patient would extend diagnostic possibilities. The bone age test could then become a routine screening test. Changing the method of determining the bone age would also prevent the patient from taking a dose of ionizing radiation, making the test less invasive. Methods: The regions of interest containing the wrist area and the epiphyses of the radius are marked on the magnetic resonance imaging of the non-dominant hand of boys aged 9 to 17 years. Textural features are computed for these regions, as it is assumed that the texture of the wrist image contains information about bone age. Results: The regression analysis revealed that there is a high correlation between the bone age of a patient and the MRI-derived textural features derived from MRI. For DICOM T1-weighted data, the best scores reached 0.94 R2, 0.46 RMSE, 0.21 MSE, and 0.33 MAE. Conclusions: The experiments performed have shown that using the MRI images gives reliable results in the assessment of bone age while not exposing the patient to ionizing radiation.

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Automatic Whole-body Bone Age Assessment Using Deep Hierarchical Features

Cited by 2 publications

References 13 publications

Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

Texture Analysis for the Bone Age Assessment from MRI Images of Adolescent Wrists in Boys

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