Abstract
Particle urinary sediment analysis in microscopic images can help doctors assess patients with kidney and urinary tract disease. Manual urine sediment inspection is labor intensive, subjective and time consuming, and traditional automated algorithms often extract handcrafted identification features. In this paper, instead of using manual extraction of features, we use CNN to learn features in an end-to-end manner to identify urine particles. In this paper, urine particle recognition is used as object detection processing, and other advanced target detection algorithms, Faster R-cnn, SSD, and deep learning frameworks such as RestNet and DenseNet, are used to study a new target detection method for urine particle recognition.
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Kouri, T., Fogazzi, G., Gant, V., Hallander, H., Hofmann, W., Guder, W.: European urinalysis guidelines. Scand. J. Clin. Lab. Invest. Suppl. 60(231), 1–96 (2000)
Ince, F.D., Ellidağ, H.Y., Koseoğlu, M., Şimşek, N., Yalçın, H., Zengin, M.O.: The comparison of automated urine analyzers with manual microscopic examination for urinalysis automated urine analyzers and manual urinalysis. Pract. Lab. Med. 5(2), 14–20 (2016)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, Alexander C.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real time object detection. In: Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6517-6525 (2017)
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. In: IEEE Conference on Computer Vision and Pattern Recognition (2018)
Luo, H., Ma, S., Wu, D., Xu, Z.: Mumford-shah segmentation for microscopic image of the urinary sediment. In: International Conference on Bioinformatics and Biomedical Engineering, pp. 861–863. IEEE (2007)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)
Li, C., Fang, B., Wang, Y., Lu, G., Qian, J., Chen, L.: Automatic detecting and recognition of casts in urine sediment images. In: International Conference on Wavelet Analysis and Pattern Recognition, pp. 26–31. IEEE (2009)
Zhang, Z., Xia, S., Duan, H.: Cellular neural network based urinary image segmentation. In: International Conference on Natural Computation, vol. 2, pp. 285–289. IEEE (2007)
Zhang, S., Wang, J., Zhao, S., Luan, X.: Urinary sediment images segmentation based on efficient gabor flters. In: International Conference on Complex Medical Engineering, pp. 812–815. IEEE (2007)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241 (2015)
Aziz, A., Pande, H., Cheluvaraju, B., Dastidar, T.R.: Improved Extraction of Objects from Urine Microscopy Images with Unsupervise
Lin, T.-Y., Dollar, 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)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE Computer Society, Washington, DC (2016)
Huang, G., Liu, Z., Weinberger, K.Q., et al.: Densely connected convolutional networks. In: IEEE conference on Computer Vision and Pattern Recognition, pp. 4700-4708 (2016)
Acknowledgement
This work is supported by Anhui Provincial Natural Science Foundation (grant number 1608085MF136) and Natural Science Foundation of China (grant number 61872004).
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Xu, XT., Zhang, J., Chen, P., Wang, B., Xia, Y. (2019). Urine Sediment Detection Based on Deep Learning. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_52
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DOI: https://doi.org/10.1007/978-3-030-26763-6_52
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