Legarreta Jon Haitz, Lan Zhou, Chen Yuqian, Zhang Fan, Yeterian Edward, Makris Nikos, Rushmore Jarrett, Rathi Yogesh, O'Donnell Lauren J
Department of Radiology, Brigham and Women's Hospital, Mass General Brigham/Harvard Medical School, Boston MA, USA.
Center for Clinical Investigation, Brigham and Women's Hospital, Mass General Brigham, Boston MA, USA.
bioRxiv. 2025 Mar 11:2025.03.10.642452. doi: 10.1101/2025.03.10.642452.
The fine-grained segmentation of cerebellar structures is an essential step towards supplying increasingly accurate anatomically informed analyses, including, for example, white matter diffusion magnetic resonance imaging (MRI) tractography. Cerebellar tissue segmentation is typically performed on structural magnetic resonance imaging data, such as T1-weighted data, while connectivity between segmented regions is mapped using diffusion MRI tractography data. Small deviations in structural to diffusion MRI data co-registration may negatively impact connectivity analyses. Reliable segmentation of brain tissue performed directly on diffusion MRI data helps to circumvent such inaccuracies. Diffusion MRI enables the computation of many image contrasts, including a variety of tissue microstructure maps. While multiple methods have been proposed for the segmentation of cerebellar structures using diffusion MRI, little attention has been paid to the systematic evaluation of the performance of different available input image contrasts for the segmentation task. In this work, we evaluate and compare the segmentation performance of diffusion MRI-derived contrasts on the cerebellar segmentation task. Specifically, we include spherical mean (diffusion-weighted image average) and b0 (non-diffusion-weighted image average) contrasts, local signal parameterization contrasts (diffusion tensor and kurtosis fit maps), and the structural T1-weighted MRI contrast that is most commonly employed for the task. We train a popular deep-learning architecture using a publicly available dataset (HCP-YA), leveraging cerebellar region labels from the atlas-based SUIT cerebellar segmentation pipeline. By training and testing using many diffusion-MRI-derived image inputs, we find that the spherical mean image computed from b=1000 s/mm shell data provides stable performance across different metrics and significantly outperforms the tissue microstructure contrasts that are traditionally used in machine learning segmentation methods for diffusion MRI.
小脑结构的细粒度分割是实现越来越精确的解剖学信息分析的关键一步，例如白质扩散磁共振成像（MRI）纤维束成像。小脑组织分割通常在结构磁共振成像数据（如T1加权数据）上进行，而分割区域之间的连通性则使用扩散MRI纤维束成像数据进行映射。结构MRI数据与扩散MRI数据配准中的小偏差可能会对连通性分析产生负面影响。直接在扩散MRI数据上进行可靠的脑组织分割有助于避免此类不准确情况。扩散MRI能够计算多种图像对比度，包括各种组织微结构图。虽然已经提出了多种使用扩散MRI分割小脑结构的方法，但对于分割任务中不同可用输入图像对比度性能的系统评估却很少受到关注。在这项工作中，我们评估并比较了扩散MRI衍生对比度在小脑分割任务中的分割性能。具体而言，我们纳入了球形均值（扩散加权图像平均值）和b0（非扩散加权图像平均值）对比度、局部信号参数化对比度（扩散张量和峰度拟合图），以及该任务最常用的结构T1加权MRI对比度。我们使用一个公开可用的数据集（HCP-YA）训练一种流行的深度学习架构，利用基于图谱的SUIT小脑分割管道中的小脑区域标签。通过使用许多扩散MRI衍生的图像输入进行训练和测试，我们发现从b = 1000 s/mm壳数据计算出的球形均值图像在不同指标上提供了稳定的性能，并且显著优于传统机器学习分割方法中用于扩散MRI的组织微结构对比度。
