Computer-aided medical image analysis plays a significant role in assisting medical practitioners for expert clinical diagnosis and deciding the optimal treatment plan. At present, convolutional neural networks (CNN) are the preferred choice for medical image analysis. In addition, with the rapid advancements in three-dimensional (3D) imaging systems and the availability of excellent hardware and software support to process large volumes of data, 3D deep learning methods are gaining popularity in medical image analysis. Here, we present an extensive review of the recently evolved 3D deep learning methods in medical image segmentation. Furthermore, the research gaps and future directions in 3D medical image segmentation are discussed.
翻译:计算机辅助医疗图像分析在协助医生进行专家临床诊断和决定最佳治疗计划方面发挥了重要作用,目前,进化神经网络是医学图像分析的首选选择,此外,随着三维(3D)成像系统的快速发展,并随着用于处理大量数据的优秀硬件和软件支持的提供,3D深层学习方法在医学图像分析中越来越受欢迎。在这里,我们广泛审查了医学图像分割中最近形成的3D深层学习方法。此外,还讨论了3D医学图像分割的研究差距和未来方向。