背景:使用神经影像学技术对大脑结构和功能进行的非侵入性研究越来越多地用于其临床和研究角度。大脑多个区域和结构的形态和体积变化与神经疾病如阿尔茨海默病的预后有关。癫痫,精神分裂症,等。早期识别这种变化可能具有巨大的临床意义。将三维脑磁共振图像准确分割为组织类型(即灰质,白质,脑脊液)和大脑结构,因此,具有巨大的重要性,因为它们可以作为早期生物标志物。手动分割虽然被认为是“黄金标准”,但很耗时,主观,不适合更大的神经影像学研究。多年来,已经开发了几种自动分割工具和算法;机器学习模型,特别是使用深度卷积神经网络(CNN)架构的机器学习模型越来越多地用于提高自动方法的准确性。
目的:研究的目的是了解自动分割工具的当前和新兴状态,他们的比较,机器学习模型,其可靠性,和缺点,旨在专注于改进方法和算法的开发。
方法:该研究的重点是对公开的神经影像学工具的回顾,他们的比较,以及新兴的机器学习模型,特别是基于过去五年开发和发布的CNN架构的模型。
结论:由不同研究小组开发并公开用于大脑自动分割的几个软件工具在几个比较研究中显示了其结果的变异性,并且没有达到临床研究所需的可靠性水平。机器学习模型,特别是三维全卷积网络模型,可以提供与公共可用工具相关的健壮和有效的替代方案,但在看不见的数据集上表现不佳。与培训相关的挑战,计算成本,再现性,需要解决机器学习模型的不同扫描模式的验证问题。
BACKGROUND: The noninvasive study of the structure and functions of the brain using neuroimaging techniques is increasingly being used for its clinical and research perspective. The morphological and volumetric changes in several regions and structures of brains are associated with the prognosis of neurological disorders such as Alzheimer\'s disease, epilepsy, schizophrenia, etc. and the early identification of such changes can have huge clinical significance. The accurate segmentation of three-dimensional brain magnetic resonance images into tissue types (i.e., grey matter, white matter, cerebrospinal fluid) and brain structures, thus, has huge importance as they can act as early biomarkers. The manual segmentation though considered the \"gold standard\" is time-consuming, subjective, and not suitable for bigger neuroimaging studies. Several automatic segmentation tools and algorithms have been developed over the years; the machine learning models particularly those using deep convolutional neural network (CNN) architecture are increasingly being applied to improve the accuracy of automatic methods.
OBJECTIVE: The purpose of the study is to understand the current and emerging state of automatic segmentation tools, their comparison, machine learning models, their reliability, and shortcomings with an intent to focus on the development of improved methods and algorithms.
METHODS: The study focuses on the
review of publicly available neuroimaging tools, their comparison, and emerging machine learning models particularly those based on CNN architecture developed and published during the last five years.
CONCLUSIONS: Several software tools developed by various research groups and made publicly available for automatic segmentation of the brain show variability in their results in several comparison studies and have not attained the level of reliability required for clinical studies. The machine learning models particularly three dimensional fully convolutional network models can provide a robust and efficient alternative with relation to publicly available tools but perform poorly on unseen datasets. The challenges related to training, computation cost, reproducibility, and validation across distinct scanning modalities for machine learning models need to be addressed.