PRSNet: Part Relation and Selection Network for Bone Age Assessment
Bone age is one of the most important indicators for assessing bone’s maturity, which can help to interpret human’s growth development level and potential progress. In the clinical practice, bone age assessment (BAA) of X-ray images requires the joint consideration of the appearance and location information of hand bones. These kinds of information can be effectively captured by the relation of different anatomical parts of hand bone. Recently developed methods differ mostly in how they model the part relation and choose useful parts for BAA. However, these methods neglect the mining of relationship among different parts, which can help to improve the assessment accuracy. In this paper, we propose a novel part relation module, which accurately discovers the underlying concurrency of parts by using multi-scale context information of deep learning feature representation. Furthermore, based on the part relation, we explore a new part selection module, which comprehensively measures the importance of parts and select the top ranking parts for assisting BAA. We jointly train our part relation and selection modules in an end-to-end way, achieving state-of-the-art performance on the public RSNA 2017 Pediatric Bone Age benchmark dataset and outperforming other competitive methods by a significant margin.
We thank the anonymous reviewers for their constructive comments. This work was supported in part by NSFC (61702338) and Shenzhen Science and Technology Program (No. JCYJ20180507182410327).
- 1.Bae, B.U., Bae, W., Jung, K.H.: Improved deep learning model for bone age assessment using triplet ranking loss (2018)Google Scholar
- 5.Greulich, W.W., Pyle, S.I., Todd, T.W.: Radiographic Atlas of Skeletal Development of the Hand and Wrist, vol. 2. Stanford University Press, Stanford (1959)Google Scholar
- 7.He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
- 8.Iglovikov, V.I., Rakhlin, A., Kalinin, A.A., Shvets, A.A.: Paediatric bone age assessment using deep convolutional neural networks. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 300–308. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_34CrossRefGoogle Scholar
- 10.Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)Google Scholar
- 12.Tanner, J.M., Whitehouse, R., Cameron, N., Marshall, W., Healy, M., Goldstein, H., et al.: Assessment of Skeletal Maturity and Prediction of Adult Height (TW2 Method), vol. 16. Academic press, London (1975)Google Scholar
- 14.Wu, E., et al.: Residual attention based network for hand bone age assessment. arXiv preprint arXiv:1901.05876 (2018)