Liver tumor segmentation method based on U-Net architecture: a review

Biao Wang, Chunfeng Yang
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Abstract

Liver cancer is a disease with a high incidence and high probability of deterioration, and for the rapid diagnosis of liver disease, CT scans must be used to segment the liver tumors. For the past few years, with the rapid development of deep learning, many deep learning methods for liver tumor segmentation using abdominal computed tomography (CT) images have appeared, and the clinical application of these methods is of important significance for computer-aided diagnosis of liver tumors. The U-Net, with its unique U-shape network structure, exhibits excellent performance in medical image segmentation field and has been extensively utilized in various medical image segmentation applications. In this paper, we summarize the researches of U-Net and its improved networks in CT image segmentation of liver tumors by deep learning methods and classify various U-Net-based convolutional neural networks (CNNs) into 2D (two-dimensional), 3D (three-dimensional), and 2.5D (2.5-dimensional). In this paper, 2D, 3D, and 2.5D convolutional neural networks are summarized. In addition, this paper summarizes the advantages and disadvantages as well as the improvement methods of each type of network, which provides a useful reference for the studies of deep learning based on liver tumor segmentation field. Finally, this paper envisions future research trends for deep learning segmentation methods in the context of liver tumors.
基于 U-Net 架构的肝脏肿瘤分割方法:综述
肝癌是一种发病率高、恶化概率高的疾病,为了快速诊断肝病,必须利用CT扫描对肝脏肿瘤进行分割。几年来,随着深度学习的快速发展,出现了许多利用腹部计算机断层扫描(CT)图像进行肝脏肿瘤分割的深度学习方法,这些方法的临床应用对于肝脏肿瘤的计算机辅助诊断具有重要意义。U-Net 以其独特的 U 型网络结构,在医学图像分割领域表现出卓越的性能,被广泛应用于各种医学图像分割应用中。本文总结了 U-Net 及其改进网络在利用深度学习方法进行肝脏肿瘤 CT 图像分割方面的研究,并将各种基于 U-Net 的卷积神经网络(CNN)分为 2D(二维)、3D(三维)和 2.5D(2.5 维)。本文总结了 2D、3D 和 2.5D 卷积神经网络。此外,本文还总结了各类网络的优缺点和改进方法,为基于肝脏肿瘤分割领域的深度学习研究提供了有益的参考。最后,本文展望了深度学习分割方法在肝脏肿瘤方面的未来研究趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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