Renal ultrasound image segmentation method based on channel attention and GL-UNet11

• Published in: Journal of radiation research and applied sciences Vol. 16; no. 3; p. 100631
• Main Authors: Chen, Shao-Hua, Wu, Yan-Ling, Pan, Can-Yu, Lian, Luo-Yu, Su, Qi-Chen
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2023
• Subjects: Deep learning; GL-Net; Kidney; Parenchyma of kidney; Renal sinuses; Semantic segmentation; Deep learning; GL-Net; Renal sinuses; Semantic segmentation; Parenchyma of kidney; Kidney
• ISSN: 1687-8507; 1687-8507
• DOI: 10.1016/j.jrras.2023.100631

Kidney tumor is one of the common malignant tumors of urinary system. However, rapid and accurate segmentation of kidney tissue is still a challenging problem in the medical image processing field on account of its obscure onset and uncertain and discontinuous boundary of its anatomical structure. We proposed a deep learning method globa-local UNet11 (GL-UNet11) for ultrasonic image segmentation of kidney, renal parenchyma and renal sinus, aiming at the inaccuracy of internal and marginal segmentation in ultrasonic image segmentation. We deepen the UNet network to make it more expressive, and propose a new channel attention network, Global Local network (GL-UNet), which takes into account the influence of global channel and local channel on predicting the importance of each channel, so that the network can better focus on important information. At the same time, the proposed global-local network is added to the convolutional block of the deepened UNet subsampling part, which effectively enhances the features of the important channels. The Dice coefficient and the intersection over union (IOU) of our method reached 96.25% and 92.78% in the task of kidney segmentation. In the task of segmentating renal parenchyma, Dice coefficient reached 92.90% and IOU reached 86.75%. In the task of segmentating renal sinus, Dice coefficient reached 90.18% and IOU reached 82.12%, both of which were superior to other deep learning methods. The GL-UNet11 network proposed in this study compared with the previously proposed UNet and Segnet methods and SE-UNet, ECA-UNet and GL-UNet methods proposed in this paper, Great improvement has been made in both quantitative and qualitative parts. It has achieved good segmentation effect on the kidney segmentation data set, which is of great significance for the early detection and prognosis of kidney patients in the future.