Bone Age Assessment in Young Children Using Automatic Carpal Bone Feature Extraction and Support Vector Regression

Somkantha, Krit; Theera-Umpon, Nipon; Auephanwiriyakul, Sansanee

doi:10.1007/s10278-011-9372-3

Cited by 51 publications

(25 citation statements)

References 29 publications

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“…The conventional BAA approaches could be categorized into GP and TW methods. Some traditional machine learning methods have been applied to a BAA approach, such as support vector machine (SVM) [4], SVM with cross-correlation [5], and support vector regression [6]. Besides, the most prevalent and widely used software for automatic BAA in Europe is BoneXpert [7].…”

Section: Review Of Baamentioning

confidence: 99%

Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset

Pan

Zhao

Chen

et al. 2020

International Journal of Biomedical Imaging

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Bone age assessment (BAA) is an essential topic in the clinical practice of evaluating the biological maturity of children. Because the manual method is time-consuming and prone to observer variability, it is attractive to develop computer-aided and automated methods for BAA. In this paper, we present a fully automatic BAA method. To eliminate noise in a raw X-ray image, we start with using U-Net to precisely segment hand mask image from a raw X-ray image. Even though U-Net can perform the segmentation with high precision, it needs a bigger annotated dataset. To alleviate the annotation burden, we propose to use deep active learning (AL) to select unlabeled data samples with sufficient information intentionally. These samples are given to Oracle for annotation. After that, they are then used for subsequential training. In the beginning, only 300 data are manually annotated and then the improved U-Net within the AL framework can robustly segment all the 12611 images in RSNA dataset. The AL segmentation model achieved a Dice score at 0.95 in the annotated testing set. To optimize the learning process, we employ six off-the-shell deep Convolutional Neural Networks (CNNs) with pretrained weights on ImageNet. We use them to extract features of preprocessed hand images with a transfer learning technique. In the end, a variety of ensemble regression algorithms are applied to perform BAA. Besides, we choose a specific CNN to extract features and explain why we select that CNN. Experimental results show that the proposed approach achieved discrepancy between manual and predicted bone age of about 6.96 and 7.35 months for male and female cohorts, respectively, on the RSNA dataset. These accuracies are comparable to state-of-the-art performance.

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Section: Review Of Baamentioning

confidence: 99%

Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset

Pan

Zhao

Chen

et al. 2020

International Journal of Biomedical Imaging

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“…Diversos pesquisadores desenvolveram soluções para automatizar a AIO utilizando processamento de imagens GERTYCH;LIU, 2007;THODBERG et al, 2009; SOMKANTHA; THEERA-UMPON; AUEPHANWIRIYAKUL, 2011;SEOK et al, 2012).…”

Section: Trabalho Relacionadounclassified

End-to-End Bone Age Assessment with Residual Learning

Souza

Oliveira

2018

2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)

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Bone age is a reliable metric for determining the level of biological maturity of children and adolescents. Its assessment is a crucial part of the diagnosis of a variety of pediatric syndromes that affect growth, such as endocrine disorders. The most commonly used method for bone age assessment (BAA) is still based on the comparison of the patient's hand and wrist radiograph to a bone age atlas. Such a method, however, takes considerable time, requires an expert rater, and suffers from high inter-rater variability, thus impacting a precise diagnostic. We present a deep-learning-based approach to estimate bone age from radiographs. It provides a fast, deterministic solution for bone age assessment. We demonstrate the effectiveness of our method by using it to rate a set of 200 radiographs and comparing the results against ground truth. This experiment has shown that our method's performance is similar to the one of a trained physician. Our system is available on-line, providing a free global service for doctors working in remote areas or in institutions with no specialized radiologists as well as for the general population.

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“…The correct rates of an error tolerance within 1.5 years of age are 63% and 57% for both male and female children, respectively. In 2011, Somkantha et al [15] applied an automatic bone age determination system to 180 left-hand digital images between 0 and 6 years. They used image enhancement, region of interest selection, and boundary extraction in the image-processing procedure.…”

Section: Literature Reviewmentioning

confidence: 99%

Bone age determination in young children (newborn to 6 years old) using support vector machines

Güraksın¹,

Uğuz²,

Baykan³

2016

Turk J Elec Eng & Comp Sci

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Abstract:Bone age is assessed through a radiological analysis of the left-hand wrist and is then compared to chronological age. A conflict between these two values indicates an abnormality in the development process of the skeleton. This study, conducted on children aged between 0 and 6 years, proposes a computer-based diagnostic system to eliminate the disadvantages of the methods used in bone age determination. For this purpose, primarily an image processing procedure was applied to the X-ray images of the left-hand wrist of children from different ethnic groups aged between 0 and 6 years. A total of 9 features, corresponding to the carpal bones and distal epiphysis of the radius bone with some physiological attributes of the children, were obtained. Then, by using gain ratio, the best 6 features were used for the classification process. Next, the bone age determination process was performed with the obtained features with the help of the support vector machine (SVM), naive Bayes, k-nearest neighborhood, and C4.5 algorithms. Finally, the features used in the determination process and their effects on the accuracies were examined. The results of the designed system showed that SVM method has a better achievement rate than the other methods at a rate of 72.82%. Additionally, in this study, a new feature corresponding to the distance between the centers of gravity of the carpal bones was used for the classification process, and the analysis of the related feature showed that there was a statistically significant difference at P <0.05 between this feature and bones in children aged between 0 and 6 years.

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Bone Age Assessment in Young Children Using Automatic Carpal Bone Feature Extraction and Support Vector Regression

Cited by 51 publications

References 29 publications

Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset

Fully Automated Bone Age Assessment on Large-Scale Hand X-Ray Dataset

End-to-End Bone Age Assessment with Residual Learning

Bone age determination in young children (newborn to 6 years old) using support vector machines

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