Citation: | MAO Keji, WU Kunxiu, LU Wei, CHEN Lijian, MAO Jiafa. A Study of the CHN Intelligent Bone Age Assessment Method with Reference to Atlas Developmental Indications[J]. Journal of Electronics & Information Technology, 2023, 45(3): 958-967. doi: 10.11999/JEIT211577 |
[1] |
潘复平, 张国栋. 骨龄与青春期发育关系的追踪观察[J]. 中华预防医学杂志, 1985, 19(2): 79–82.
PAN Fuping and ZHANG Guodong. Follow-up observation on the relationship between bone age and puberty development[J]. Chinese Journal of Preventive Medicine, 1985, 19(2): 79–82.
|
[2] |
邵伟东, 金春华, 潘慧, 等. 中国儿童手腕部骨龄评测标准CHN法与参考图谱[M]. 北京: 中国协和医科大学出版社, 2018: 15–20.
SHAO Weidong, JIN Chunhua, PAN Hui, et al. Chinese Children's Wrist Bone Age Evaluation Standard[M]. Beijing: Peking Union Medical College Press, 2018: 15–20.
|
[3] |
张烨城. 骨龄在体育教学及训练中的应用[J]. 青少年体育, 2018(9): 58–59. doi: 10.3969/j.issn.2095-4581.2018.09.037
ZHANG Yecheng. Application of bone age in physical education teaching and training[J]. Youth Sport, 2018(9): 58–59. doi: 10.3969/j.issn.2095-4581.2018.09.037
|
[4] |
沈勋章. 手腕部骨龄鉴定方法的研究进展[J]. 中国医药科学, 2011, 1(12): 9–12.
SHEN Xunzhang. Identification of wrist skeletal age of the research progress[J]. China Medicine and Pharmacy, 2011, 1(12): 9–12.
|
[5] |
SPAMPINATO C, PALAZZO S, GIORDANO D, et al. Deep learning for automated skeletal bone age assessment in X-ray images[J]. Medical Image Analysis, 2017, 36: 41–51. doi: 10.1016/j.media.2016.10.010
|
[6] |
GREULICH W W and IDELL PYLE S. Radiographic atlas of skeletal development of the hand and wrist[J]. The American Journal of the Medical Sciences, 1959, 238(3): 393.
|
[7] |
TANNER J M and WHITEHOUSE R H. Clinical longitudinal standards for height, weight, height velocity, weight velocity, and stages of puberty[J]. Archives of Disease in Childhood, 1976, 51(3): 170–179. doi: 10.1136/adc.51.3.170
|
[8] |
叶义言. 新版骨龄评分法概述[J]. 中华儿科杂志, 2004, 42(1): 30–32. doi: 10.3760/j.issn:0578-1310.2004.01.009
YE Yiyan. Overview of new version of bone age scoring method[J]. Chinese Journal of Pediatrics, 2004, 42(1): 30–32. doi: 10.3760/j.issn:0578-1310.2004.01.009
|
[9] |
张绍岩, 花纪青, 刘丽娟, 等. 中国人手腕骨发育标准—中华05. III. 中国儿童骨发育的长期趋势[J]. 中国运动医学杂志, 2007, 26(2): 149–153. doi: 10.16038/j.1000-6710.2007.02.004
ZHANG Shaoyan, HUA Jiqing, LIU Lijuan, et al. The standards of skeletal maturity of hand and wrist for Chinese-China 05. III. The secular trend of skeletal development in Chinese children[J]. Chinese Journal of Sports Medicine, 2007, 26(2): 149–153. doi: 10.16038/j.1000-6710.2007.02.004
|
[10] |
张绍岩, 杨士增, 邵伟东, 等. 中国人手腕骨发育标准—CHN法[J]. 体育科学, 1993, 13(6): 33–39.
ZHANG Shaoyan, YANG Shizeng, SHAO Weidong, et al. The standards of skeletal development of hand and wrist for Chinese-CHN method[J]. China Sports Science, 1993, 13(6): 33–39.
|
[11] |
THODBERG H H, KREIBORG S, JUUL A, et al. The BoneXpert method for automated determination of skeletal maturity[J]. IEEE Transactions on Medical Imaging, 2009, 28(1): 52–66. doi: 10.1109/tmi.2008.926067
|
[12] |
LEE H, TAJMIR S, LEE J, et al. Fully automated deep learning system for bone age assessment[J]. Journal of Digital Imaging, 2017, 30(4): 427–441. doi: 10.1007/s10278-017-9955-8
|
[13] |
OQUAB M, BOTTOU L, LAPTEV I, et al. Learning and transferring mid-level image representations using convolutional neural networks[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 1717–1724.
|
[14] |
REN Xuhua, LI Tingting, YANG Xiujun, et al. Regression convolutional neural network for automated pediatric bone age assessment from hand radiograph[J]. IEEE Journal of Biomedical and Health Informatics, 2019, 23(5): 2030–2038. doi: 10.1109/jbhi.2018.2876916
|
[15] |
HAN Yaxin and WANG Guangbin. Skeletal bone age prediction based on a deep residual network with spatial transformer[J]. Computer Methods and Programs in Biomedicine, 2020, 197: 105754. doi: 10.1016/j.cmpb.2020.105754
|
[16] |
LIU Bo, ZHANG Yu, CHU Meicheng, et al. Bone age assessment based on rank-monotonicity enhanced ranking CNN[J]. IEEE Access, 2019, 7: 120976–120983. doi: 10.1109/access.2019.2937341
|
[17] |
SON S J, SONG Y, KIM N, et al. TW3-based fully automated bone age assessment system using deep neural networks[J]. IEEE Access, 2019, 7: 33346–33358. doi: 10.1109/access.2019.2903131
|
[18] |
刘宗才, 吴锦华, 王荣品, 等. 深度学习骨龄评测系统对贵州省儿童及青少年骨龄测评的准确性[J]. 中国医学影像技术, 2019, 35(12): 1799–1803. doi: 10.13929/j.1003-3289.201907037
LIU Zongcai, WU Jinhua, WANG Rongpin, et al. Accuracy of deep learning based bone age assessment system of children and adolescents in Guizhou[J]. Chinese Journal of Medical Imaging Technology, 2019, 35(12): 1799–1803. doi: 10.13929/j.1003-3289.201907037
|
[19] |
REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149. doi: 10.1109/tpami.2016.2577031
|
[20] |
KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84–90. doi: 10.1145/3065386
|
[21] |
LIU Jun, WANG Guang, DUAN Lingyu, et al. Skeleton-based human action recognition with global context-aware attention LSTM networks[J]. IEEE Transactions on Image Processing, 2018, 27(4): 1586–1599. doi: 10.1109/tip.2017.2785279
|
[22] |
WANG Jinrui, LI Shunming, AN Zenghui, et al. Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines[J]. Neurocomputing, 2019, 329: 53–65. doi: 10.1016/j.neucom.2018.10.049
|
[23] |
DAHL G E, SAINATH T N, and HINTON G E. Improving deep neural networks for LVCSR using rectified linear units and dropout[C]. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, Canada, 2013: 8609–8613.
|
[24] |
KO B, KIM H G, OH K J, et al. Controlled dropout: A different approach to using dropout on deep neural network[C]. 2017 IEEE International Conference on Big Data and Smart Computing, Jeju, Korea (South), 2017: 358–362.
|
[25] |
WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional block attention module[C]. The 15th European Conference on Computer Vision, Munich, Germany, 2018: 3–19.
|
[26] |
HU Jie, SHEN Li, ALBANIE S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011–2023. doi: 10.1109/TPAMI.2019.2913372
|
[27] |
WANG Qilong, WU Banggu, ZHU Pengfei, et al. ECA-Net: Efficient channel attention for deep convolutional neural networks[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 11531–11539.
|
[28] |
LIU Haomiao, WANG Ruiping, SHAN Shiguang, et al. Deep supervised hashing for fast image retrieval[J]. International Journal of Computer Vision, 2019, 127(9): 1217–1234. doi: 10.1007/s11263-019-01174-4
|
[29] |
WU E, KONG Bin, WANG Xin, et al. Residual attention based network for hand bone age assessment[C]. 2019 IEEE 16th International Symposium on Biomedical Imaging, Venice, Italy, 2018: 1158–1161.
|