Abstract：
Hippocampal atrophy (tissue loss) has become a fundamental outcome parameter in clinical trials on Alzheimer\'s disease. To accurately estimate hippocampus volume and track its volume loss, a robust and reliable segmentation is essential. Manual hippocampus segmentation is considered the gold standard but is extensive, time-consuming, and prone to rater bias. Therefore, it is often replaced by automated programs like FreeSurfer, one of the most commonly used tools in clinical research. Recently, deep learning-based methods have also been successfully applied to hippocampus segmentation. The basis of all approaches are clinically used T1-weighted whole-brain MR images with approximately 1 mm isotropic resolution. However, such T1 images show low contrast-to-noise ratios (CNRs), particularly for many hippocampal substructures, limiting delineation reliability. To overcome these limitations, high-resolution T2-weighted scans are suggested for better visualization and delineation, as they show higher CNRs and usually allow for higher resolutions. Unfortunately, such time-consuming T2-weighted sequences are not feasible in a clinical routine. We propose an automated hippocampus segmentation pipeline leveraging deep learning with T2-weighted MR images for enhanced hippocampus segmentation of clinical T1-weighted images based on a series of 3D convolutional neural networks and a specifically acquired multi-contrast dataset. This dataset consists of corresponding pairs of T1- and high-resolution T2-weighted images, with the T2 images only used to create more accurate manual ground truth annotations and to train the segmentation network. The T2-based ground truth labels were also used to evaluate all experiments by comparing the masks visually and by various quantitative measures. We compared our approach with four established state-of-the-art hippocampus segmentation algorithms (FreeSurfer, ASHS, HippoDeep, HippMapp3r) and demonstrated a superior segmentation performance. Moreover, we found that the automated segmentation of T1-weighted images benefits from the T2-based ground truth data. In conclusion, this work showed the beneficial use of high-resolution, T2-based ground truth data for training an automated, deep learning-based hippocampus segmentation and provides the basis for a reliable estimation of hippocampal atrophy in clinical studies.
摘要：
海马萎缩（组织丢失）已成为阿尔茨海默病临床试验的基本结果参数。为了准确估计海马体积并跟踪其体积损失，一个强大的和可靠的分割是必不可少的。手动海马体分割被认为是黄金标准，但很广泛，耗时,容易出现评分者偏见。因此,它经常被像FreeSurfer这样的自动化程序所取代，临床研究中最常用的工具之一。最近,基于深度学习的方法也已成功应用于海马体分割。所有方法的基础都是临床上使用的T1加权全脑MR图像，具有大约1mm的各向同性分辨率。然而,这样的T1图像显示低对比度噪声比(CNRs)，特别是对于许多海马亚结构，限制轮廓的可靠性。为了克服这些限制,高分辨率T2加权扫描建议更好的可视化和描绘，因为它们显示更高的CNR，通常允许更高的分辨率。不幸的是,这种耗时的T2加权序列在临床常规中是不可行的.我们提出了一种基于一系列3D卷积神经网络和专门获取的多对比度数据集，利用T2wMR图像的深度学习来增强临床T1加权图像的海马分割的自动化海马分割流水线。该数据集由相应的高分辨率T1和T2加权图像对组成，T2图像仅用于创建更准确的手动地面实况注释并训练分割网络。基于T2的地面实况标签也用于通过视觉比较掩模和通过各种定量测量来评估所有实验。我们将我们的方法与四种已建立的最先进的海马体分割算法(FreeSurfer，ASHS,HippoDeep,HippMapp3r)并展示了卓越的分割性能。此外，我们发现,T1加权图像的自动分割得益于基于T2的地面实况数据.总之,这项工作显示了高分辨率的有益使用，基于T2的地面实况数据，用于训练自动化，基于深度学习的海马区分割，为临床研究中海马萎缩的可靠估计提供基础。
