DSEU-net: A novel deep supervision SEU-net for medical ultrasound image segmentation

Abstract

The automatic and accurate medical ultrasound image segmentation has been a challenging task due to the coupled interference of various internal and external factors. In recent years, CNN techniques have been widely and successfully used in medical image segmentation. Motivated by this, this paper proposes a novel squeeze-and-excitation attention U-net with deep supervision (DSEU-net) for medical ultrasound image segmentation. Specifically, a deeper U-net is first used as a benchmark network to capture sufficient target feature information from complex ultrasound images. Then, the squeeze-and-excitation (SE) block is regarded as the bond between encoder and decoder to enhance the attention to useful object regions. Moreover, the introduction of SE block not only strengthens the association of useful information at a distance, but also suppresses the introduction of irrelevant information. Finally, the deep supervised constraints are added to the decoding stage of the network to refine the prediction masks of ultrasound images. Extensive experimental results on three clinical ultrasound datasets show that DSEU-net has better robustness and superiority in ultrasound image segmentation. In the segmentation of the first breast ultrasound dataset (BUSI), the values of Jaccard, Precision, Recall, Specificity and Dice are 70.36%, 79.73%, 82.70%, 97.42% and 78.51%, respectively. The values of Jaccard, Precision, Recall, Specificity and Dice for our method on the second breast ultrasound dataset (Dataset B) are 73.17%, 82.58%, 84.02%, 99.05% and 81.50%, respectively. For the segmentation of kidney ultrasound dataset (KUS), the values of Jaccard, Precision, Recall, Specificity, Dice, HD, ASSD and ABD are 89.47, 94.77, 94.36, 99.10, 94.32, 12.42, 0.48 and 3.44, respectively. Comparing with the original U-net, DSEU-net improved on average 8.28% and 12.55% on five metrics for two breast ultrasound data. DSEU-net improved on average 54.81% on eight metrics for the kidney ultrasound dataset.