TY - GEN
T1 - Comparison of Semantic Segmentation Methods on Renal Ultrasounds Images
AU - Zhang, Qimin
AU - Wang, Qiang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - There are many people with chronic kidney disease in China, so manual segmentation can not meet the huge social needs. Due to the ability to accurately segment the images, deep learning methods can be used for the detection of kidney diseases. In this paper, a total of 881 renal ultrasound images were collected and labelled. Four semantic segmentation networks, including FCN, U-Net, SegNet and Deeplab were used to segment renal ultrasound images. In order to measure the segmentation effect of different networks, two common indicators, PA and IoU, were used to evaluate the results. The results showed that all the four semantic segmentation networks achieved good results in renal ultrasound image segmentation, among which Deeplab had the best effect on the test set, with PA reaching 99.14% and IoU reaching 0.8219.
AB - There are many people with chronic kidney disease in China, so manual segmentation can not meet the huge social needs. Due to the ability to accurately segment the images, deep learning methods can be used for the detection of kidney diseases. In this paper, a total of 881 renal ultrasound images were collected and labelled. Four semantic segmentation networks, including FCN, U-Net, SegNet and Deeplab were used to segment renal ultrasound images. In order to measure the segmentation effect of different networks, two common indicators, PA and IoU, were used to evaluate the results. The results showed that all the four semantic segmentation networks achieved good results in renal ultrasound image segmentation, among which Deeplab had the best effect on the test set, with PA reaching 99.14% and IoU reaching 0.8219.
KW - Data augmentation
KW - Deep learning
KW - Image segmentation
KW - Kidney disease detection
KW - Semantic segmentation network
UR - https://www.scopus.com/pages/publications/85134433702
U2 - 10.1109/I2MTC48687.2022.9806525
DO - 10.1109/I2MTC48687.2022.9806525
M3 - 会议稿件
AN - SCOPUS:85134433702
T3 - Conference Record - IEEE Instrumentation and Measurement Technology Conference
BT - I2MTC 2022 - IEEE International Instrumentation and Measurement Technology Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2022
Y2 - 16 May 2022 through 19 May 2022
ER -