Research on medical image segmentation algorithm based on deep learning

Abstract

For the diagnosis of cancer and tumors, medical pathological image analysis is critical. Whereas, due to the diversity and complexity of pathological data, the segmentation task is faced with challenges such as blurred edges, less training data, difficulty in feature extraction and case segmentation. The advancement of deep learning technologies has resulted in breakthrough achievements in medical image analysis by its powerful feature learning, flexible design, and other characteristics, and it has widely applied. In recent years, many scholars have improved the classic segmentation method. By combining various segmentation methods, segmentation efficiency has effectively improved, and the improved algorithm makes up for the defects of the original segmentation method. In this review, we evaluated and examined the recent research accomplishments in medical picture segmentation using various deep learning approaches, as well as the future research directions for medical image segmentation using deep learning.