Segmentation of brain structures on MRI is the primary step for further quantitative analysis of brain diseases. Manual segmentation is still considered the gold standard in terms of accuracy; however, such data is extremely time-consuming to generate. This paper presents a deep learning-based segmentation approach for 12 deep-brain structures, utilizing multiple region-based U-Nets. The brain is divided into three focal regions of interest that encompass the brainstem, the ventricular system, and the striatum. Next, three region-based U-nets are run in parallel to parcellate these larger structures into their respective four substructures. This approach not only greatly reduces the training and processing times but also significantly enhances the segmentation accuracy, compared to segmenting the entire MRI image at once. Our approach achieves remarkable accuracy with an average Dice Similarity Coefficient (DSC) of 0.901 and 95% Hausdorff Distance (HD95) of 1.155 mm. The method was compared with state-of-the-art segmentation approaches, demonstrating a high level of accuracy and robustness of the proposed method.

翻译：磁共振成像中的脑结构分割是进一步定量分析脑部疾病的首要步骤。手动分割在准确性方面仍被视为金标准，但此类数据的生成极为耗时。本文提出一种基于深度学习的深部脑结构分割方法，针对12个深部脑结构，采用多个基于区域的U-Net。将大脑划分为三个聚焦关注区域，分别涵盖脑干、脑室系统和纹状体。随后，三个基于区域的U-Net并行运行，将这些较大结构分割为各自的四个子结构。与一次性分割整个MRI图像相比，该方法不仅大幅缩短了训练与处理时间，还显著提升了分割精度。我们的方法取得了卓越的准确率，平均Dice相似系数（DSC）为0.901，95%豪斯多夫距离（HD95）为1.155毫米。该方法与当前最先进的分割方法进行了对比，证明了所提方法具有较高的准确性和鲁棒性。