journal article·2025Open access

Attention Eraser and Quantitative Measures for Automated Bone Age Assessment

Liuqiang Shu, Lei Yu

Computers, Materials & Continua· Volume 82· Issue 1

Abstract

Bone age assessment (BAA) aims to determine whether a child’s growth and development are normal concerning their chronological age. To predict bone age more accurately based on radiographs, and for the left-hand X-ray image... | Find, read and cite all the research you need on Tech Science Press
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OpenAlex topics: Forensic Anthropology and Bioarchaeology Studies

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References (24)

Show source-provided references
1. Carpal length in children—A useful measurement in the diagnosis of rheumatoid arthritis and some congenital malformation syndromes10.1148/129.3.661 ↗
2. Interrelationships of skeletal maturation, sexual development and somatic growth in man10.1080/03014467400000031 ↗
3. Radiographic atlas of skeletal development of the hand and wrist10.1097/00000441-195909000-00030 ↗
4. Assessment of skeletal maturity and prediction of adult height (TW3 method)
5. Effect of training on replicability of assessments of skeletal maturity (Greulich-Pyle)10.2214/ajr.108.3.511 ↗
6. Generative adversarial network based data augmentation and gender-last training strategy with application to bone age assessment10.1016/j.cmpb.2021.106456 ↗
7. Bone age assessment of children using a digital hand atlas10.1016/j.compmedimag.2007.02.012 ↗
8. Bone age assessment using support vector regression with smart class mapping
9. A computerized image analysis system for estimating Tanner-Whitehouse 2 bone age10.1159/000184210 ↗
10. Digital hand atlas and web-based bone age assessment: System design and implementation10.1016/S0895-6111(00)00026-4 ↗
11. Computer automated approach to the extraction of epiphyseal regions in hand radiographs
12. Correlation between skeletal age and metacarpal bones and metacarpophalangeal joints dimensions
13. Feature description with SIFT, SURF, BRIEF, BRISK, or FREAK? A general question answered for bone age assessment10.1016/j.compbiomed.2015.11.006 ↗
14. L. M. Davis, B. J. Theobald, and A. Bagnall, “Automated bone age assessment using feature extraction,” presented at the 13th Int. Conf. Intell. Data Eng. Autom. Learn. (IDEAL), Natal, Brazil, Aug. 29–31, 2012.10.1007/978-3-642-32639-4_6 ↗
15. M. Zhang, D. Wu, Q. Liu, Q. Li, Y. Zhan and X. S. Zhou, “Multi-task convolutional neural network for joint bone age assessment and ossification center detection from hand radiograph,” presented at the 10th Int. Workshop Mach. Learn. Med. Imaging (MLMI), Shenzhen, China, Oct. 13, 2019.10.1007/978-3-030-32692-0_78 ↗
16. Y. Ji, H. Chen, D. Lin, X. Wu, and D. Lin, “PRSNet: Part relation and selection network for bone age assessment,” presented at the 22nd Int. Conf. Med. Image Comput. Comput.-Assist. Interv. (MICCAI), Shenzhen, China, Oct. 13–17, 2019.10.1007/978-3-030-32226-7_46 ↗
17. Attention-guided discriminative region localization and label distribution learning for bone age assessment10.1109/JBHI.2021.3095128 ↗
18. Computer assisted bone age estimation using dimensions of metacarpal bones and metacarpophalangeal joints based on neural network
19. Development of a simplified new method of bone age estimation using three bones of the hand and wrist10.1007/s12020-024-03684-9 ↗
20. K. Jung, T. D. Nguyen, D. -T. Le, J. Bum, S. S. Woo and H. Choo, “Hand bone X-rays segmentation and congregation for age assessment using deep learning,” presented at the 2023 Int. Conf. Inf. Netw. (ICOIN), Bangkok, Thailand, 2023.10.1109/ICOIN56518.2023.10048972 ↗
21. M. Escobar, C. González, F. Torres, L. Daza, G. Triana and P. Arbeláez, “Hand pose estimation for pediatric bone age assessment,” presented at the 22nd Int. Conf. Med. Image Comput. Comput.-Assist. Interv. (MICCAI), Shenzhen, China, Oct. 13–17, 2019.10.1007/978-3-030-32226-7_59 ↗
22. Research on an intelligent evaluation method of bone age based on multi-region combination
23. A. Palkar, S. Shanbhog, and J. Medikonda, “Bone age estimation of pediatrics by analyzing hand X-rays using deep learning technique,” presented at the 2023 Int. Conf. Recent Adv. Inf. Technol. Sustain. Dev. (ICRAIS), Manipal, India, 2023.10.1109/ICRAIS59684.2023.10367114 ↗
24. R. G. V. Prasanna, M. F. Shaik, L. V. Sastry, C. G. Sahithi, J. Jagadeesh and P. V. Rao, “Deep learning-based bone age assessment from hand X-rays: An evaluation and analysis,” presented at the 2023 Int. Conf. Data Sci., Agents Artif. Intell. (ICDSAAI), Chennai, India, 2023.10.1109/ICDSAAI59313.2023.10452582 ↗