CC-Net: Consistency Learning Combined with Contrastive Learning for Kidney Ultrasound Segmentation

Due to the scarcity of medical images with labels and the fact that medical image data is not easily accessible. Kidney ultrasound (KUS) images have speckle noise, low image quality, acoustic shadows, and other reasons, which pose significant challenges to the kidney ultrasound image segmentation task. To solve the above two problems, a novel consistency learning and contrastive learning-based kidney ultrasound image segmentation method (CC-Net) has been proposed. Specifically, the student-teacher model was adopted as the semi-supervised learning framework and uncertainty estimation was introduced. The Consensus Uncertainty Map (CUM) strategy was proposed to generate segmentation results for the teacher model when calculating uncertainty. To solve the problems of acoustic shadows and low image quality in kidney ultrasound images, a kidney ultrasound image data augmentation method (KUS-AUG) and a convolutional block (KUS-CONV) tailored to the characteristics of kidney ultrasound images were proposed. In addition, contrastive learning was implemented to enhance the encoder's ability to learn more semantic information, due to its inherent advantage in learning data distribution. Finally, a series of experiments were conducted on the ultrasound image dataset and the method achieved the best performance.