基于深度学习的医学图像分割技术研究进展.

Medical image segmentation plays an important role in clinical diagnosis and is the basis of other medical image processing methods. With the improvement of computer hardware performance, image segmentation technology based on deep learning has already become a powerful tool for processing medical images and is widely used in various medical image segmentation tasks. This paper introduces several types of common medical images and their characteristics, analyzes and compares the image segmentation algorithms that have emerged in recent years. Some algorithms have been successfully applied to segmentation tasks such as brain tissue, lungs and blood vessels. In response to the current problems in the development of medical image segmentation technology based on deep learning, corresponding strategies are proposed, and the future development direction is also prospected. [ABSTRACT FROM AUTHOR]