Segmentation of brain structures on magnetic resonance imaging (MRI) is a
highly relevant neuroimaging topic, as it is a prerequisite for different
analyses such as volumetry or shape analysis. Automated segmentation
facilitates the study of brain structures in larger cohorts when compared with
manual segmentation, which is time-consuming. However, the development of most
automated methods relies on large and manually annotated datasets, which limits
the generalizability of these methods. Recently, new techniques using synthetic
images have emerged, reducing the need for manual annotation. Here we provide
HELM, Hypothalamic ex vivo Label Maps, a dataset composed of label maps built
from publicly available ultra-high resolution ex vivo MRI from 10 whole
hemispheres, which can be used to develop segmentation methods using synthetic
data. The label maps are obtained with a combination of manual labels for the
hypothalamic regions and automated segmentations for the rest of the brain, and
mirrored to simulate entire brains. We also provide the pre-processed ex vivo
scans, as this dataset can support future projects to include other structures
after these are manually segmented.

通过使用合成图像构建的标签图，我们提供了一个由高分辨率离体磁共振成像构成的数据集，该数据集可以用于开发使用合成数据的脑结构分割方法。

用于分割模型训练的下丘脑及其子区域的高分辨率分割

High-resolution segmentations of the hypothalamus and its subregions for  training of segmentation models

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.

基于深度学习的分割方法可在较短的时间内高效准确地将大脑结构从 MRI 图像中分割出来，该方法以多区域 U-Net 为基础，将大脑分为三个感兴趣的区域，并通过并行运行三个区域 U-Net 将这些更大的结构进一步细分为各自的四个子结构。与一次性分割整个 MRI 图像相比，该方法不仅大大减少了训练和处理时间，而且显著提高了分割的准确性。本方法在平均 Dice 相似系数 (DSC) 为 0.901、95% 豪斯多夫距离 (HD95) 为 1.155 毫米的情况下取得了卓越的准确性，并与最先进的分割方法进行了比较，展示了所提出方法的高准确性和鲁棒性。

基于区域的 U-net 用于加速训练和提高深度脑部分割的精度

Region-based U-net for accelerated training and enhanced precision in  deep brain segmentation

Accuracy segmentation of brain structures could be helpful for glioma and
radiotherapy planning. However, due to the visual and anatomical differences
between different modalities, the accurate segmentation of brain structures
becomes challenging. To address this problem, we first construct a residual
block based U-shape network with a deep encoder and shallow decoder, which can
trade off the framework performance and efficiency. Then, we introduce the
Tversky loss to address the issue of the class imbalance between different
foreground and the background classes. Finally, a model ensemble strategy is
utilized to remove outliers and further boost performance.

使用带残差块的 U 型网络和 Tversky 损失函数进行脑结构分割，提高在不同模态下的准确率，同时采用模型集成策略进一步提高性能。