scholarly journals Automatic Radiographic Bone Age Assessment Using Deep Joint Learning with Attention Modules

2020 ◽  
Author(s):  
Wei Tang ◽  
Gang Wu ◽  
Gang Shen
Keyword(s):  
Large Scale ◽  
Critical Role ◽  
Network Models ◽  
Bone Age ◽  
Semantic Features ◽  
Age Assessment ◽  
Joint Learning ◽  
Bone Age Assessment ◽  
Network Backbone ◽  
And Gender

Hand and wrist skeletal radiographs serve as an important medium for diversified medical and forensic tasks involving bone age assessment. As an alternative to traditional atlas-based bone age identification techniques, deep learning algorithms automatically classify the radiographs into predefined bone age classes, provided that the deep neural networks (DNN) have been well trained with large scale annotated datasets. Most of the current bone age classification DNNs directly explore the existing network models developed for other computer vision representations and understanding applications, such as VGG, Inception, and ResNet. In this work, we present a multi-scale attention-enhanced classifier with a convolutional neural network backbone, specifically designed for bone age prediction and trained to learn a subject’s bone age and gender jointly. The proposed classifier is trained with the dataset provided by the RSNA machine learning challenge, and the low-level semantic features are then transferred to a smaller Tongji dataset collected from a hospital in China. As demonstrated by the experiments, the proposed classifier achieves the MADs of 0.41 years over RSNA data and 0.36 years on Tongji data, outperforming other single model state-of-the-art and baseline algorithms for the same test. It illustrates that joint learning of gender information plays a critical role in refining the bone age assessment, while the convolution-based attention mechanism helps retrieve the key features.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaoying Pan ◽  
Yizhe Zhao ◽  
Hao Chen ◽  
De Wei ◽  
Chen Zhao ◽  
...  
Keyword(s):  
Large Scale ◽  
Bone Age ◽  
Sufficient Information ◽  
Deep Convolutional Neural Networks ◽  
Age Assessment ◽  
X Ray ◽  
Bone Age Assessment ◽  
Biological Maturity ◽  
In The Beginning ◽  
Automated Methods

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.

scholarly journals Bone age assessment: a large scale comparison of the Greulich and Pyle, and Tanner and Whitehouse (TW2) methods

1999 ◽  
Vol 81 (2) ◽  
pp. 172-173 ◽  
Author(s):  
R K Bull ◽  
P D Edwards ◽  
P M Kemp ◽  
S Fry ◽  
I A Hughes
Keyword(s):  
Large Scale ◽  
Bone Age ◽  
Age Assessment ◽  
Greulich And Pyle ◽  
Bone Age Assessment

Validation study of automated bone age assessment in 1285 children and adolescents aged 5 to 16 years

2019 ◽  
Author(s):  
Klara Maratova ◽  
Dana Zemkova ◽  
Jan Lebl ◽  
Ondrej Soucek ◽  
Stepanka Pruhova ◽  
...  
Keyword(s):  
Children And Adolescents ◽  
Validation Study ◽  
Bone Age ◽  
Age Assessment ◽  
Bone Age Assessment

scholarly journals Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 765
Author(s):  
Mohd Asyraf Zulkifley ◽  
Nur Ayuni Mohamed ◽  
Siti Raihanah Abdani ◽  
Nor Azwan Mohamed Kamari ◽  
Asraf Mohamed Moubark ◽  
...  
Keyword(s):  
Bone Growth ◽  
Bone Age ◽  
Attention Mechanism ◽  
Assessment System ◽  
Dominant Hand ◽  
Age Assessment ◽  
Image Normalization ◽  
X Ray ◽  
Bone Age Assessment ◽  
Skeletal Bone

Skeletal bone age assessment using X-ray images is a standard clinical procedure to detect any anomaly in bone growth among kids and babies. The assessed bone age indicates the actual level of growth, whereby a large discrepancy between the assessed and chronological age might point to a growth disorder. Hence, skeletal bone age assessment is used to screen the possibility of growth abnormalities, genetic problems, and endocrine disorders. Usually, the manual screening is assessed through X-ray images of the non-dominant hand using the Greulich–Pyle (GP) or Tanner–Whitehouse (TW) approach. The GP uses a standard hand atlas, which will be the reference point to predict the bone age of a patient, while the TW uses a scoring mechanism to assess the bone age using several regions of interest information. However, both approaches are heavily dependent on individual domain knowledge and expertise, which is prone to high bias in inter and intra-observer results. Hence, an automated bone age assessment system, which is referred to as Attention-Xception Network (AXNet) is proposed to automatically predict the bone age accurately. The proposed AXNet consists of two parts, which are image normalization and bone age regression modules. The image normalization module will transform each X-ray image into a standardized form so that the regressor network can be trained using better input images. This module will first extract the hand region from the background, which is then rotated to an upright position using the angle calculated from the four key-points of interest. Then, the masked and rotated hand image will be aligned such that it will be positioned in the middle of the image. Both of the masked and rotated images will be obtained through existing state-of-the-art deep learning methods. The last module will then predict the bone age through the Attention-Xception network that incorporates multiple layers of spatial-attention mechanism to emphasize the important features for more accurate bone age prediction. From the experimental results, the proposed AXNet achieves the lowest mean absolute error and mean squared error of 7.699 months and 108.869 months2, respectively. Therefore, the proposed AXNet has demonstrated its potential for practical clinical use with an error of less than one year to assist the experts or radiologists in evaluating the bone age objectively.

Creation and Validation of a Shorthand Magnetic Resonance Imaging Bone Age Assessment Tool of the Knee as an Alternative Skeletal Maturity Assessment

2021 ◽  
pp. 036354652110329
Author(s):  
Cary S. Politzer ◽  
James D. Bomar ◽  
Hakan C. Pehlivan ◽  
Pradyumna Gurusamy ◽  
Eric W. Edmonds ◽  
...  
Keyword(s):  
Assessment Tool ◽  
Skeletal Maturity ◽  
Bone Age ◽  
Mri Scan ◽  
Perfect Agreement ◽  
Age Assessment ◽  
Left Hand ◽  
Knee Mri ◽  
Bone Age Assessment ◽  
Hand Radiograph

Background: In managing pediatric knee conditions, an accurate bone age assessment is often critical for diagnostic, prognostic, and treatment purposes. Historically, the Greulich and Pyle atlas (hand atlas) has been the gold standard bone age assessment tool. In 2013, a shorthand bone age assessment tool based on this atlas (hand shorthand) was devised as a simpler and more efficient alternative. Recently, a knee magnetic resonance imaging (MRI) bone age atlas (MRI atlas) was created to circumvent the need for a left-hand radiograph. Purpose: To create a shorthand version of the knee MRI atlas. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A shorthand bone age assessment method was created utilizing the previously published MRI atlas, which utilizes several criteria that are visualized across a series of images. The MRI shorthand draws on characteristic criteria for each age that are best observed on a single MRI scan. For validation, we performed a retrospective assessment of skeletally immature patients. One reader performed the bone age assessment using the MRI atlas and the MRI shorthand on 200 patients. Then, 4 readers performed the bone age assessment with the hand atlas, hand shorthand, MRI atlas, and MRI shorthand on a subset of 22 patients in a blinded fashion. All 22 patients had a knee MRI scan and a left-hand radiograph within 4 weeks of each other. Interobserver and intraobserver reliability, as well as variability among observers, were evaluated. Results: A total of 200 patients with a mean age of 13.5 years (range, 9.08-17.98 years) were included in this study. Also, 22 patients with a mean age of 13.3 years (range, 9.0-15.6 years) had a knee MRI scan and a left-hand radiograph within 4 weeks. The intraobserver and interobserver reliability of all 4 assessment tools were acceptable (intraclass correlation coefficient [ICC] ≥ 0.8; P < .001). When comparing the MRI shorthand with the MRI atlas, there was excellent agreement (ICC = 0.989), whereas the hand shorthand compared with the hand atlas had good agreement (ICC = 0.765). The MRI shorthand also had perfect agreement in 50% of readings among all 4 readers, and 95% of readings had agreement within 1 year, whereas the hand shorthand had perfect agreement in 32% of readings and 77% agreement within 1 year. Conclusion: The MRI shorthand is a simple and efficient means of assessing the skeletal maturity of adolescent patients with a knee MRI scan. This bone age assessment technique had interobserver and intraobserver reliability equivalent to or better than the standard method of utilizing a left-hand radiograph.

scholarly journals Usefulness of the Sauvegrain Method of Bone Age Assessment in Indian Children

2020 ◽  
Author(s):  
Premal Naik ◽  
Dhren Ganjwala ◽  
Chhaya Bhatt ◽  
Kranti Suresh Vora
Keyword(s):  
Bone Age ◽  
Indian Children ◽  
Age Assessment ◽  
Bone Age Assessment

Bone age assessment with conventional ultrasonography in healthy infants from 1 to 24 months of age

2015 ◽  
Vol 45 (7) ◽  
pp. 1007-1015 ◽  
Author(s):  
Monica Daneff ◽  
Claudia Casalis ◽  
Claudio H. Bruno ◽  
Didier A. Bruno
Keyword(s):  
Bone Age ◽  
Age Assessment ◽  
Healthy Infants ◽  
Bone Age Assessment
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