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CAST:一种基于多尺度卷积神经网络的自动化海马亚区分割工具箱。

CAST: A multi-scale convolutional neural network based automated hippocampal subfield segmentation toolbox.

机构信息

Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, 89106, USA.

Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, 89106, USA; Department of Psychology and Neuroscience, University of Colorado, Boulder, CO, 80309, USA.

出版信息

Neuroimage. 2020 Sep;218:116947. doi: 10.1016/j.neuroimage.2020.116947. Epub 2020 May 29.

Abstract

In this study, we developed a multi-scale Convolutional neural network based Automated hippocampal subfield Segmentation Toolbox (CAST) for automated segmentation of hippocampal subfields. Although training CAST required approximately three days on a single workstation with a high-quality GPU card, CAST can segment a new subject in less than 1 ​min even with GPU acceleration disabled, thus this method is more time efficient than current automated methods and manual segmentation. This toolbox is highly flexible with either a single modality or multiple modalities and can be easily set up to be trained with a researcher's unique data. A 3D multi-scale deep convolutional neural network is the key algorithm used in the toolbox. The main merit of multi-scale images is the capability to capture more global structural information from down-sampled images without dramatically increasing memory and computational burden. The original images capture more local information to refine the boundary between subfields. Residual learning is applied to alleviate the vanishing gradient problem and improve the performance with a deeper network. We applied CAST with the same settings on two datasets, one 7T dataset (the UMC dataset) with only the T2 image and one 3T dataset (the MNI dataset) with both T1 and T2 images available. The segmentation accuracy of both CAST and the state-of-the-art automated method ASHS, in terms of the dice similarity coefficient (DSC), were comparable. CAST significantly improved the reliability of segmenting small subfields, such as CA2, CA3, and the entorhinal cortex (ERC), in terms of the intraclass correlation coefficient (ICC). Both ASHS and manual segmentation process some subfields (e.g. CA2 and ERC) with high DSC values but low ICC values, consequently increasing the difficulty of judging segmentation quality. CAST produces very consistent DSC and ICC values, with a maximal discrepancy of 0.01 (DSC-ICC) across all subfields. The pre-trained model, source code, and settings for the CAST toolbox are publicly available.

摘要

在这项研究中,我们开发了一种基于多尺度卷积神经网络的自动海马亚区分割工具箱(CAST),用于自动分割海马亚区。虽然在具有高质量 GPU 卡的单个工作站上训练 CAST 大约需要三天时间,但即使禁用 GPU 加速,CAST 也可以在不到 1 分钟的时间内分割一个新的对象,因此这种方法比当前的自动化方法和手动分割更节省时间。该工具箱具有高度的灵活性,可以使用单一模态或多种模态,并且可以轻松设置为使用研究人员的独特数据进行训练。三维多尺度深度卷积神经网络是该工具箱中使用的关键算法。多尺度图像的主要优点是能够从下采样图像中捕获更多的全局结构信息,而不会显著增加内存和计算负担。原始图像捕获更多的局部信息来细化亚区之间的边界。残差学习用于缓解消失梯度问题,并通过更深的网络提高性能。我们在两个数据集上应用了相同的 CAST 设置,一个是只有 T2 图像的 7T 数据集(UMC 数据集),另一个是既有 T1 又有 T2 图像的 3T 数据集(MNI 数据集)。在骰子相似系数(DSC)方面,CAST 和最先进的自动化方法 ASHS 的分割准确性相当。CAST 在内部类相关系数(ICC)方面显著提高了分割小亚区(如 CA2、CA3 和内嗅皮层(ERC))的可靠性。ASHS 和手动分割过程中的一些亚区(如 CA2 和 ERC)的 DSC 值较高,但 ICC 值较低,因此增加了判断分割质量的难度。CAST 产生非常一致的 DSC 和 ICC 值,所有亚区的最大差异为 0.01(DSC-ICC)。CAST 工具包的预训练模型、源代码和设置都是公开可用的。

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