scholarly journals Automated Bone Age Assessment: Motivation, Taxonomies, and Challenges

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Marjan Mansourvar ◽  
Maizatul Akmar Ismail ◽  
Tutut Herawan ◽  
Ram Gopal Raj ◽  
Sameem Abdul Kareem ◽  
...  
Keyword(s):  
Bone Age ◽  
Future Research ◽  
Age Assessment ◽  
X Ray ◽  
Left Hand ◽  
Bone Age Assessment ◽  
Manual Methods ◽  
Biological Maturity ◽  
Automated Methods ◽  
Experimental Stage

Bone age assessment (BAA) of unknown people is one of the most important topics in clinical procedure for evaluation of biological maturity of children. BAA is performed usually by comparing an X-ray of left hand wrist with an atlas of known sample bones. Recently, BAA has gained remarkable ground from academia and medicine. Manual methods of BAA are time-consuming and prone to observer variability. This is a motivation for developing automated methods of BAA. However, there is considerable research on the automated assessment, much of which are still in the experimental stage. This survey provides taxonomy of automated BAA approaches and discusses the challenges. Finally, we present suggestions for future research.

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 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 Bone age assessment by dual-energy X-ray absorptiometry in children: an alternative for X-ray?

2012 ◽  
Vol 85 (1010) ◽  
pp. 114-120 ◽  
Author(s):  
D H M Heppe ◽  
H R Taal ◽  
G D S Ernst ◽  
E L T Van Den Akker ◽  
M M H Lequin ◽  
...  
Keyword(s):  
Bone Age ◽  
Dual Energy ◽  
Age Assessment ◽  
X Ray ◽  
Bone Age Assessment

A Localization and Extraction Method Research for Phalange ROI and Carpal ROI from Left Hand Radiography

2011 ◽  
Vol 340 ◽  
pp. 259-265
Author(s):  
Long Ke Ran ◽  
Ling He ◽  
Zhong Chen
Keyword(s):  
Prediction Method ◽  
Bone Age ◽  
Third Order ◽  
Shape Information ◽  
Age Assessment ◽  
Left Hand ◽  
Key Points ◽  
Order Polynomial ◽  
Bone Age Assessment ◽  
Hand Radiographs

In the research of Automatic bone age assessment,the most efficient location and successful extraction of regions of interest(ROI) from hand radiographs is one of the most difficult and important key technologies. Based on using shape information for phalanges and carpals, a background prediction method is propoesd , which uses a two-dimensional third order polynomial linear regression to fit background. And we also localize the key points of carpal and phalange ROI by usingK-cosine algorithm, finally we extract the carpal and phalange ROI successfully and properly. Through experiments, the proposed method resulted in over 93% correct extraction from more than 60 left hand radiograph data. The proposed method is robust to gray value variation of background and the position and orientation of the hand, so it can be used directly for automatic bone age assessment in the following study.

scholarly journals Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

2017 ◽  
Author(s):  
Vladimir Iglovikov ◽  
Alexander Rakhlin ◽  
Alexandr A. Kalinin ◽  
Alexey Shvets
Keyword(s):  
Skeletal Development ◽  
Bone Age ◽  
Deep Convolutional Neural Networks ◽  
Age Assessment ◽  
Left Hand ◽  
Radiological Society ◽  
Hand Bones ◽  
Bone Age Assessment ◽  
Using Data ◽  
Common Clinical Practice

AbstractSkeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from the 2017 Pediatric Bone Age Challenge organized by the Radiological Society of North America. The dataset for this competition consists of 12,600 radiological images. Each radiograph in this dataset is an image of a left hand labeled with bone age and sex of a patient. Our approach utilizes several deep neural network architectures trained end-to-end. We use images of whole hands as well as specific parts of a hand for both training and prediction. This approach allows us to measure the importance of specific hand bones for automated bone age analysis. We further evaluate the performance of the suggested method in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.

scholarly journals Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology

2021 ◽  
Author(s):  
Amaka C. Offiah
Keyword(s):  
Artificial Intelligence ◽  
Pediatric Radiology ◽  
Bone Age ◽  
Future Research ◽  
Fracture Detection ◽  
Musculoskeletal Radiology ◽  
Age Assessment ◽  
Adult World ◽  
Bone Age Assessment

AbstractArtificial intelligence (AI) is playing an ever-increasing role in radiology (more so in the adult world than in pediatrics), to the extent that there are unfounded fears it will completely take over the role of the radiologist. In relation to musculoskeletal applications of AI in pediatric radiology, we are far from the time when AI will replace radiologists; even for the commonest application (bone age assessment), AI is more often employed in an AI-assist mode rather than an AI-replace or AI-extend mode. AI for bone age assessment has been in clinical use for more than a decade and is the area in which most research has been conducted. Most other potential indications in children (such as appendicular and vertebral fracture detection) remain largely in the research domain. This article reviews the areas in which AI is most prominent in relation to the pediatric musculoskeletal system, briefly summarizing the current literature and highlighting areas for future research. Pediatric radiologists are encouraged to participate as members of the research teams conducting pediatric radiology artificial intelligence research.

Deep learning for automated skeletal bone age assessment in X-ray images

2017 ◽  
Vol 36 ◽  
pp. 41-51 ◽  
Author(s):  
C. Spampinato ◽  
S. Palazzo ◽  
D. Giordano ◽  
M. Aldinucci ◽  
R. Leonardi
Keyword(s):  
Deep Learning ◽  
Bone Age ◽  
Age Assessment ◽  
X Ray ◽  
Bone Age Assessment ◽  
Skeletal Bone

scholarly journals A deep learning-based computer-aided diagnosis method of X-ray images for bone age assessment

2021 ◽  
Author(s):  
Shaowei Li ◽  
Bowen Liu ◽  
Shulian Li ◽  
Xinyu Zhu ◽  
Yang Yan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Bone Age ◽  
Computer Aided Diagnosis ◽  
Experimental Comparison ◽  
Age Assessment ◽  
X Ray ◽  
Computer Aided ◽  
Bone Age Assessment ◽  
Diagnosis Method ◽  
Aided Diagnosis

AbstractBone age assessment using hand-wrist X-ray images is fundamental when diagnosing growth disorders of a child or providing a more patient-specific treatment. However, as clinical procedures are a subjective assessment, the accuracy depends highly on the doctor’s experience. Motivated by this, a deep learning-based computer-aided diagnosis method was proposed for performing bone age assessment. Inspired by clinical approaches and aimed to reduce expensive manual annotations, informative regions localization based on a complete unsupervised learning method was firstly performed and an image-processing pipeline was proposed. Subsequently, an image model with pre-trained weights as a backbone was utilized to enhance the reliability of prediction. The prediction head was implemented by a Multiple Layer Perceptron with one hidden layer. In compliance with clinical studies, gender information was an additional input to the prediction head by embedded into the feature vector calculated from the backbone model. After the experimental comparison study, the best results showed a mean absolute error of 6.2 months on the public RSNA dataset and 5.1 months on the additional dataset using MobileNetV3 as the backbone.

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