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.

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 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 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 Rethinking Greulich and Pyle: A Deep Learning Approach to Pediatric Bone Age Assessment Using Pediatric Trauma Hand Radiographs

2020 ◽  
Vol 2 (4) ◽  
pp. e190198
Author(s):  
Ian Pan ◽  
Grayson L. Baird ◽  
Simukayi Mutasa ◽  
Derek Merck ◽  
Carrie Ruzal-Shapiro ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pediatric Trauma ◽  
Bone Age ◽  
Learning Approach ◽  
Age Assessment ◽  
Greulich And Pyle ◽  
Bone Age Assessment ◽  
Hand Radiographs

scholarly journals Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Rui Liu ◽  
Yuanyuan Jia ◽  
Xiangqian He ◽  
Zhe Li ◽  
Jinhua Cai ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Bone Age ◽  
Classification Problem ◽  
Mean Value ◽  
Classification Model ◽  
Age Assessment ◽  
Bone Age Assessment ◽  
Segmentation Task ◽  
Hand Radiographs ◽  
Hand Radiograph

In the study of pediatric automatic bone age assessment (BAA) in clinical practice, the extraction of the object area in hand radiographs is an important part, which directly affects the prediction accuracy of the BAA. But no perfect segmentation solution has been found yet. This work is to develop an automatic hand radiograph segmentation method with high precision and efficiency. We considered the hand segmentation task as a classification problem. The optimal segmentation threshold for each image was regarded as the prediction target. We utilized the normalized histogram, mean value, and variance of each image as input features to train the classification model, based on ensemble learning with multiple classifiers. 600 left-hand radiographs with the bone age ranging from 1 to 18 years old were included in the dataset. Compared with traditional segmentation methods and the state-of-the-art U-Net network, the proposed method performed better with a higher precision and less computational load, achieving an average PSNR of 52.43 dB, SSIM of 0.97, DSC of 0.97, and JSI of 0.91, which is more suitable in clinical application. Furthermore, the experimental results also verified that hand radiograph segmentation could bring an average improvement for BAA performance of at least 13%.

Bone Age Assessment

2013 ◽  
Vol 2 (2) ◽  
pp. 1-10
Author(s):  
S. Kavya ◽  
Pavithra Pugalendi ◽  
Rose Martina P. A. ◽  
N. Sriraam ◽  
K. S. Babu ◽  
...  
Keyword(s):  
Skeletal Development ◽  
Back Propagation ◽  
Bone Age ◽  
Back Propagation Neural Network ◽  
Typical Feature ◽  
Age Assessment ◽  
Left Hand ◽  
Automated Method ◽  
Bone Age Assessment ◽  
The Given

Bone age assessment defined as the measure of skeletal development is most often used in pediatrics and forensics to estimate the true age of a person. It is usually done by comparing the left hand X-ray of a person with the hand radiographs in the standard atlas or based on local regions of interests (ROI) that include epiphyseal regions of the phalanges (14 ROI’s).Both these assessments were labour intensive, prone to discrepancies and can only be used to estimate the age till 18. Hence there is a need to develop automated method to assess the bone age by exploiting the appropriate features. This paper attempts to identify a procedure in recognizing the respective bone that belongs to male or female with its corresponding age. The automated procedure comprises of segmentation of metacarpals using area based statistics followed by typical feature extraction. Nine features are extracted for the experimental study. A back propagation neural network is then applied to classify whether the given sample refers to male or female bone. It is observed from the simulation results that the proposed procedure is found to be less computation burden and the results are found to be comparable with the existing work reported in the literature.

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