标题： 基于深度学习的扩散MRI脑部Desikan-Killiany分割 显示英文标题

标题： Deep Learning-Based Desikan-Killiany Parcellation of the Brain Using Diffusion MRI

摘要： 在扩散磁共振成像（dMRI）空间中准确的脑区划分对于高级神经影像分析至关重要。然而，大多数现有方法依赖于解剖磁共振成像进行分割和跨模态配准，这一过程可能会引入误差并限制该技术的通用性。在本研究中，我们提出了一种基于深度学习的新框架，仅使用扩散磁共振成像数据，直接基于Desikan-Killiany（DK）图谱进行划分。我们的方法利用了一个分层的两阶段分割网络：第一阶段将大脑划分为较大的脑区，第二阶段则细化分割，以确定每个粗略类别内的更详细子区域。我们进行了广泛的消融研究，评估了各种扩散衍生参数图，确定了各向异性分数、迹、球度和最大特征值的最佳组合，从而提高了划分的准确性。在人类连接组计划和神经精神表型联盟数据集上评估时，我们的方法相比现有的最先进模型取得了更优的Dice相似性系数。此外，我们的方法在不同图像分辨率和采集协议下表现出强大的泛化能力，通过区域内相对标准偏差测量，显示出更均匀的划分结果。这项工作代表了基于dMRI的脑分割的重要进展，提供了一个精确、可靠且无需配准的解决方案，这对于研究和临床应用中的结构连接性和微结构分析至关重要。我们方法的实现已在github.com/xmindflow/DKParcellationdMRI公开。 显示英文摘要

摘要： Accurate brain parcellation in diffusion MRI (dMRI) space is essential for advanced neuroimaging analyses. However, most existing approaches rely on anatomical MRI for segmentation and inter-modality registration, a process that can introduce errors and limit the versatility of the technique. In this study, we present a novel deep learning-based framework for direct parcellation based on the Desikan-Killiany (DK) atlas using only diffusion MRI data. Our method utilizes a hierarchical, two-stage segmentation network: the first stage performs coarse parcellation into broad brain regions, and the second stage refines the segmentation to delineate more detailed subregions within each coarse category. We conduct an extensive ablation study to evaluate various diffusion-derived parameter maps, identifying an optimal combination of fractional anisotropy, trace, sphericity, and maximum eigenvalue that enhances parellation accuracy. When evaluated on the Human Connectome Project and Consortium for Neuropsychiatric Phenomics datasets, our approach achieves superior Dice Similarity Coefficients compared to existing state-of-the-art models. Additionally, our method demonstrates robust generalization across different image resolutions and acquisition protocols, producing more homogeneous parcellations as measured by the relative standard deviation within regions. This work represents a significant advancement in dMRI-based brain segmentation, providing a precise, reliable, and registration-free solution that is critical for improved structural connectivity and microstructural analyses in both research and clinical applications. The implementation of our method is publicly available on github.com/xmindflow/DKParcellationdMRI.