Shuqiang Wang
ABSTRACT
Assessment of skeletal maturity plays an essential role in the clinical management of the adolescent disease. This task is very challenging when using machine learning method due to the limited data and large anatomical variations among different subjects. In this paper, we propose a deep learning pipeline to automatically assess the distal radius and ulna (DRU) maturity from left hand radiographs. The model we proposed acquires two convincing advantages: first, our model preserves the maximum information flow and has a much faster convergence rate. Second, our model avoids overfitting even if training with limited data. The proposed method achieves 83.33% and 90.31% for radius and ulna classification respectively.
- W. W. Greulich, S. I. Pyle, and T. W. Todd, "Radiographic atlas of skeletal development of the hand and wrist," vol. 2, 1959.Google Scholar
- J. M. Tanner and R. H. Whitehouse, "Clinical longitudinal standards for height, weight, height velocity, weight velocity, and stages of puberty." Archives of disease in childhood, vol. 51, no. 3, pp. 170--179, 1976.Google ScholarCross Ref
- R. Bull, P. Edwards, P. Kemp, S. Fry, and I. Hughes, "Bone age assessment: a large scale comparison of the greulich and pyle, and tanner and whitehouse (tw2) methods," Archives of disease in childhood, vol. 81, no. 2, pp. 172--173, 1999.Google ScholarCross Ref
- H. Goldstein, N. Cameron, J. Healy, and M. Tanner, "Assessment of skeletal maturity and predication of adult height (tw3 method)," London: WB Saunders, 2001.Google Scholar
- K. D. Luk, L. B. Saw, S. Grozman, K. M. Cheung, and D. Samartzis, "Assessment of skeletal maturity in scoliosis patients to determine clinical management: a new classification scheme using distal radius and ulna radiographs," The Spine Journal, vol. 14, no. 2, pp. 315--325, 2014.Google ScholarCross Ref
- K. Uemura, Y. Miyoshi, T. Kawahara, S. Yoneyama, Y. Hattori, J.-i. Teranishi, K. Kondo, M. Moriyama, S. Takebayashi, Y. Yokomizo et al., "Prognostic value of a computer-aided diagnosis system involving bone scans among men treated with docetaxel for metastatic castration-resistant prostate cancer," BMC cancer, vol. 16, no. 1, p. 109, 2016.Google ScholarCross Ref
- Y. Miyoshi, S. Yoneyama, T. Kawahara, Y. Hattori, J.-i. Teranishi, K. Kondo, M. Moriyama, S. Takebayashi, Y. Yokomizo, M. Yao et al., "Prognostic value of the bone scan index using a computer-aided diagnosis system for bone scans in hormone-naive prostate cancer patients with bone metastases," BMC cancer, vol. 16, no. 1, p.128, 2016.Google ScholarCross Ref
- M. Chen, "Automated bone age classification with deep neural networks." Stanford University, USA, Technical Report, 2016.Google Scholar
- J. H. Lee and K. G. Kim, "Applying deep learning in medical images: The case of bone age estimation," vol. 24, no. 1, 2018, pp. 86--92.Google Scholar
- J. Zhou, Z. Li, W. Zhi, B. Liang, D. Moses, and L. Dawes, "Using convolutional neural networks and transfer learning for bone age classification," pp. 1--6, 2017.Google Scholar
- K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," pp. 770--778, 2016.Google Scholar
- R. K. Srivastava, K. Greff, and J. Schmidhuber, "Training very deep networks," pp. 2377--2385, 2015. Google ScholarDigital Library
- G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks." vol. 1, no. 2, p. 3, 2017.Google Scholar
- S. Ren, K. He, R. Girshick, and J. Sun, "Faster r-cnn: Towards real-time object detection with region proposal networks," pp. 91--99, 2015. Google ScholarDigital Library
- J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," pp. 3431--3440, 2015. Google ScholarDigital Library
- X. Glorot, A. Bordes, and Y. Bengio, "Deep sparse rectifier neural networks," in Proceedings of the fourteenth international conference on artificial intelligence and statistics, 2011, pp. 315--323.Google Scholar
- S. S. Girija, "Tensorflow: Large-scale machine learning on heterogeneous distributed systems," 2016.Google Scholar
- Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," vol. 86, no. 11. IEEE, 1998, pp. 2278--2324.Google ScholarCross Ref
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," pp. 1097--1105, 2012. Google ScholarDigital Library
Index Terms
- Densely-Connected Deep Learning System for Assessment of Skeletal Maturity
Recommendations
Automatic bone maturity grading from EOS radiographs in Adolescent Idiopathic Scoliosis
AbstractAdolescent Idiopathic Scoliosis (AIS) is a deformation of the spine and it is routinely diagnosed using posteroanterior and lateral radiographs. The Risser sign used in skeletal maturity assessment is commonly accepted in AIS patient's ...
Graphical abstractDisplay Omitted
Highlights- Novel method for grading of skeletal maturity using machine learning in Adolescent Idiopathic Scoliosis (AIS).
An IMU-compensated skeletal tracking system using Kinect for the upper limb
The Kinect device is being increasingly used in conjunction with rehabilitative actions. However, the use of Kinect as a skeletal tracking system requires several further modifications and technological breakthroughs. This study used inertial ...
Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment
AbstractUltrasound imaging is a patient-friendly and robust technique for studying physiological and pathological muscles. An automatic deep learning (DL) system for the analysis of ultrasound images could be useful to support an expert ...
Graphical abstractDisplay Omitted
Highlights- A deep learning method for cross-sectional area segmentation in transverse musculoskeletal ultrasound images is presented.
Comments