Medical Image Segmentation based on U-Net: A Review

Abstract

Medical image analysis is performed by analyzing images obtained by medical imaging systems to solve clinical problems. The purpose is to extract effective information and improve the level of clinical diagnosis. In recent years, automatic segmentation based on deep learning (DL) methods has been widely used, where a neural network can automatically learn image features, which is in sharp contrast with the traditional manual learning method. U-net is one of the most important semantic segmentation frameworks for a convolutional neural network (CNN). It is widely used in the medical image analysis domain for lesion segmentation, anatomical segmentation, and classification. The advantage of this network framework is that it can not only accurately segment the desired feature target and effectively process and objectively evaluate medical images but also help to improve accuracy in the diagnosis by medical images. Therefore, this article presents a literature review of medical image segmentation based on U-net, focusing on the successful segmentation experience of U-net for different lesion regions in six medical imaging systems. Along with the latest advances in DL, this article introduces the method of combining the original U-net architecture with deep learning and a method for improving the U-net network.

Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

“…Segmenting blood vessels and extracting blood vessel features from fundus images are very important to improve the convenience when visiting patients, so that doctors can quickly diagnose and find diseases and improve diagnosis efficiency. In addition to its medical importance, the study of fundus vascular imaging is also an important reference in biometric identification [5]. By extracting and detecting retinal blood vessels and measuring and analysing the relevant features of blood vessels such as width, angle, and degree of curvature, several diseases mentioned above can be analysed and predicted to a great extent and used as a basis for the diagnosis of relevant diseases so that scientific disease prevention and corresponding drug treatment can be carried out [6].…”

Accurate Imputation of Greenhouse Environment Data for Data Integrity Utilizing Two-Dimensional Convolutional Neural Networks

“…That is, U-Net was optimized with a screen size of 50. U-Nets usually handle an image size of >500 × 500 because the input should be compressed and abstracted in multiple layers [ 18 , 24 ]. However, the optimal size was 50 for tabular data, which was 10% of the usual input size of U-Nets.…”

Batch Normalized Convolution Neural Network for Liver Segmentation

“…A literature review of medical image segmentation based on U-net was presented by [21]. They were focused on the successful segmentation experience of U-net in six medical imaging systems including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, X-ray, optical coherence tomography (OCT), and positron emission computed tomography (PET).…”