Review on Medical Image Segmentation Methods

Abstract

                Medical images, such as Ultrasound, X-Ray, Computed Axial Tomography (CAT) and Magnetic Resonance Imaging (MRI) are often stored in Picture Archiving and Communication Systems (PACS) and linked with other clinical information. Medical image segmentation aims to extract meaningful information such as shape, volume and motion of organs to detect abnormalities from the medical images by processing and analyzing. This paper provides a review on various medical image segmentation techniques.  U-Net is a popular deep learning based semantic image segmentation technique. The medical image segmentation methods based on U-Net strategy were discussed.