Abstract
The research on making accurate segmentation of images in ultrasound inspecting is a challenging in the medical image segmentation domain. It is tough to obtain a satisfactory segmentation of U-Net networks in deep learning. The difficulties are contributed to low contrast between detected targets and surrounding tissues, the large differences between target edges and shapes, and so forth. Based on batch-free normalization (BFN) and a residual attention block, a class of Attention Res BFN U-Net (ARB U-Net) network with a deep encoder and a shallow decoder is proposed, and the depth and the performance of the network is improved. With utilizing Dice loss and BCE loss are utilized as segmentation loss and classification loss respectively, a kind of Dice-BCE loss function is constructed on the basis of multi-task weighting strategy. 450 ultrasound images were used as the training set and another 50 images were used as the test set. The average segmentation accuracy of the test data set reached 97.1%, which is about 3% better than that of the traditional U-Net and its common variants. The experimental results show that the proposed network can significantly improve the accuracy and precision of ultrasound image segmentation of suprapatellar bursa.
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References
Qiao, M., Hu, Y., Guo, Y., et al.: Breast tumor classification based on a computerized breast imaging reporting and data system feature system. J. Ultrasound Med. 37(2), 403–415 (2018)
Zhang, Q., Xiao, Y., Suo, J., et al.: Sonoelastomics for breast tumor classification: a radiomics approach with clustering-based feature selection on sonoelastography. Ultrasound Med. Biol. 43(5), 1058–1069 (2017)
Zhou, S., Liu, T., Zhou, J., et al.: Preliminary study on the application of imaging histology in thyroid cancer. Oncol. Imaging 26(2), 102–105 (2017)
Jiawei, L., Zhaoting, S., Yi, G., et al.: Exploratory study on the predictive value of ultrasound imaging histomics for hormone receptor expression in invasive breast cancer. Oncol. Imaging 26(2), 128–135 (2017)
An, T., Guy, C., Szeverenyi, N.M., et al.: Ultrasound elastography and MR elastography for assessing liver fibrosis: Part 2, diagnostic performance, confounders, and future directions. Am. J. Roentgenol. 205(1), 33–40 (2015)
Castera, L., Vergniol, J., Foucher, J., et al.: Prospective comparison of transient elastography, Fibrotest, APRI, and liver biopsy for the assessment of fibrosis in chronic hepatitis C. Gastroenterology 128(2), 343–350 (2005)
Colli, A., Fraquelli, M., Andreoletti, M., et al.: Severe liver fibrosis or cirrhosis: accuracy of US for detection-analysis of 300 cases. Radiology 227(1), 89–94 (2003)
Aube, C., Oberti, F., Korali, N., et al.: Ultrasonographic diagnosis of hepatic fibrosis or cirrhosis. J. Hepatol. 30(3), 472–478 (1999)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Bi, R., Ji, C., Yang, Z., et al.: Residual based attention-UNet combing DAC and RMP modules for automatic liver tumor segmentation in CT. Math. Biosci. Eng. 19(5), 4703–4718 (2022)
He, K., Zhang, X., Ren, S., Sun, J., et al.: Deep residual learning for image recognition. In: CVPR (2016)
Huang, L., Zhou, Y., Wang, T., et al.: Delving into the estimation shift of batch normalization in a network. In: CVPR (2022)
Kiliçarslan, S., Celik, M.: RSigELU: a nonlinear activation function for deep neural networks. Expert Syst. Appl. 174, 114805 (2021)
Song, Z., Ma, Y., Tan, F., et al.: Hybrid dilated and recursive recurrent convolution network for time-domain speech enhancement. Appl. Sci. 12(7), 3461 (2022)
Tu, R.C., Mao, X.L., Guo, J.N., Wei, W.: Partial-softmax loss based deep hashing. In: Proceedings of the Web Conference 2021, pp. 2869–2878 (2021)
Prencipe, B., Altini, N., Cascarano, G.D., et al.: Focal dice loss-based V-Net for liver segments classification. Appl. Sci. 12(7), 3247 (2022)
Trinh, M.-N., Nguyen, N.-T., Tran, T.-T., Pham, V.-T.: A deep learning-based approach with image-driven active contour loss for medical image segmentation. In: Saraswat, M., Roy, S., Chowdhury, C., Gandomi, A.H. (eds.) Proceedings of International Conference on Data Science and Applications. LNNS, vol. 288, pp. 1–12. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-5120-5_1
Wang, S., Zhu, Y., Lee, S., et al.: Global-local attention network with multi-task uncertainty loss for abnormal lymph node detection in MR images. Med. Image Anal. 77, 102345 (2022)
Soomro, T.A., Afifi, A.J., Gao, J., et al.: Strided U-Net model: retinal vessels segmentation using dice loss. In: Digital Image Computing: Techniques and Applications 2018, pp. 1–8. IEEE (2018)
Wu, W., Zhang, X., Qiao, D., et al.: A faulty feeder selection method based on improved Hausdorff Distance Algorithm for neutral non-effectively grounded system. Electric Power Systems Research 203, 107648 (2022)
Zhang, Z., Chen, G., Wang, X., et al.: Fore-Net: efficient inlier estimation network for large-scale indoor scenario. ISPRS J. Photogramm. Remote. Sens. 184, 165–176 (2022)
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This work was supported in part by the National Natural Science Foundation of China under grant No. 61672084.
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Wang, Z., Yang, Q., Liu, H., Mao, L., Zhu, H., Gao, X. (2022). ARB U-Net: An Improved Neural Network for Suprapatellar Bursa Effusion Ultrasound Image Segmentation. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13531. Springer, Cham. https://doi.org/10.1007/978-3-031-15934-3_2
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