Expert system for automated bone age determination

Seok, Jinwoo; Kasa-Vubu, Joséphine Z.; DiPietro, Michael A.; Girard, Anouck

doi:10.1016/j.eswa.2015.12.011

Cited by 34 publications

(10 citation statements)

References 10 publications

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“…Seok et al. [13] built a classifier for each ROI based on decision rules to evaluate the contribution of the corresponding region to bone age. Gertych et al.…”

Section: Related Workmentioning

confidence: 99%

Classification of hand‐wrist maturity level based on similarity matching

Mao

Chen

Wang

et al. 2021

IET Image Processing

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Judging the maturity level of each hand-wrist reference bone is the core issue in bone age assessment. Relying on the superiority of convolutional neural networks in feature representation, deep learning is widely studied for the automatic bone age assessment. However, an efficient but complex deep learning network requests a large dataset with bone-maturitylevel labels for training, restricting its large-scale application in bone maturity classification. For this reason, we transform the bone-maturity-level classification problem into the similarity matching problem. Also, we propose a general structure based on Siamese network by merging two inputs into a two-channel input and introducing a dual attention mechanism, to create an Attentional Two-Channel Network (ATC-Net). This paper takes the intermediate phalanges III as an example to assess the performance of the similarity matching method and the ATC-Net. Experiments show that our method can perform better on small datasets, which effectively makes up for the data shortage problem. The ATC-Net used for classification significantly reduces the evaluation time compared with other classical networks. It reduces the time of assessing one sample by about 49% as compared to VGG-16. And more importantly, it achieves the highest classification accuracy of 92.74% among all investigated networks.

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“…Seok et al. [13] built a classifier for each ROI based on decision rules to evaluate the contribution of the corresponding region to bone age. Gertych et al.…”

Section: Related Workmentioning

confidence: 99%

Classification of hand‐wrist maturity level based on similarity matching

Mao

Chen

Wang

et al. 2021

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…A recent systematic review conducted by Dallora et al [24] shows that many of the automated bone age assessment methods still rely on conventional machine learning methods, where 22 out of the 26 methods have applied hand-crafted features extractor as an input to a regressor model. In [25], optimal fusion rules are analyzed in order to find the best combination weights for the 17 regions of interest, where the extracted features are then passed to a least-square classifier. Rather than using a single method to extract the features, the work presented in [26] combines three feature extractors, which are histogram of oriented gradients, local binary pattern, and scale-invariant feature transform, which is then fed to a support vector machine (SVM) classifier.…”

Section: Related Workmentioning

confidence: 99%

Automated Bone Age Assessment with Image Registration Using Hand X-ray Images

2020

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One of the methods for identifying growth disorder is by assessing the skeletal bone age. A child with a healthy growth rate will have approximately the same chronological and bone ages. It is important to detect any growth disorder as early as possible, so that mitigation treatment can be administered with less negative consequences. Recently, the most popular approach in assessing the discrepancy between bone and chronological ages is through the subjective protocol of Tanner–Whitehouse that assesses selected regions in the hand X-ray images. This approach relies heavily on the medical personnel experience, which produces a high intra-observer bias. Therefore, an automated bone age prediction system with image registration using hand X-ray images is proposed in order to complement the inexperienced doctors by providing the second opinion. The system relies on an optimized regression network using a novel residual separable convolution model. The regressor network requires an input image to be 299 × 299 pixels, which will be mapped to the predicted bone age through three modules of the Xception network. Moreover, the images will be pre-processed or registered first to a standardized and normalized pose using separable convolutional neural networks. Three steps image registration are performed by segmenting the hand regions, which will be rotated using angle calculated from four keypoints of interest, before positional alignment is applied to ensure the region of interest is located in the middle. The hand segmentation is based on DeepLab V3 plus architecture, while keypoints regressor for angle alignment is based on MobileNet V1 architecture, where both of them use separable convolution as the core operators. To avoid the pitfall of underfitting, synthetic data are generated while using various rotation angles, zooming factors, and shearing images in order to augment the training dataset. The experimental results show that the proposed method returns the lowest mean absolute error and mean squared error of 8.200 months and 121.902 months2, respectively. Hence, an error of less than one year is acceptable in predicting the bone age, which can serve as a good supplement tool for providing the second expert opinion. This work does not consider gender information, which is crucial in making a better prediction, as the male and female bone structures are naturally different.

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“…One way of understanding aging is finding a biomarker that indicates the physical age of subjects. Many studies have been conducted to try to estimate age using biological data [6][7][8][9][10][11]. Among various sources of biological data, the brain is one of the most complex and critical parts of human beings.…”

Section: Introductionmentioning

confidence: 99%

Age Estimation from fMRI Data Using Recurrent Neural Network

Gao

2022

Applied Sciences

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Finding a biomarker that indicates the subject’s age is one of the most important topics in biology. Several recent studies tried to extract a biomarker from brain imaging data including fMRI data. However, most of them focused on MRI data, which do not provide dynamics and lack attempts to apply recently proposed deep learning models. We propose a deep neural network model that estimates the age of a subject from fMRI images using a recurrent neural network (RNN), more precisely, a gated recurrent unit (GRU). However, applying neural networks is not trivial due to the high dimensional nature of fMRI data. In this work, we propose a novel preprocessing technique using the Automated Anatomical Labeling (AAL) atlas, which significantly reduces the input dimension. The proposed dimension reduction technique allows us to train our model with 640 training and validation samples from different projects under mean squared error (MSE). Finally, we obtain the correlation value of 0.905 between the predicted age and the actual age on 155 test samples. The proposed model estimates the age within the range of ±12 on most of the test samples. Our model is written in Python and is freely available for download.

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Expert system for automated bone age determination

Cited by 34 publications

References 10 publications

Classification of hand‐wrist maturity level based on similarity matching

Classification of hand‐wrist maturity level based on similarity matching

Automated Bone Age Assessment with Image Registration Using Hand X-ray Images

Age Estimation from fMRI Data Using Recurrent Neural Network

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