We present GeoSP, a parallel method that creates a parcellation of the
cortical mesh based on a geodesic distance, in order to consider gyri and sulci
topology. The method represents the mesh with a graph and performs a K-means
clustering in parallel. It has two modes of use, by default, it performs the
geodesic cortical parcellation based on the boundaries of the anatomical
parcels provided by the Desikan-Killiany atlas. The other mode performs the
complete parcellation of the cortex. Results for both modes and with different
values for the total number of sub-parcels show homogeneous sub-parcels.
Furthermore, the execution time is 82 s for the whole cortex mode and 18 s for
the Desikan-Killiany atlas subdivision, for a parcellation into 350
sub-parcels. The proposed method will be available to the community to perform
the evaluation of data-driven cortical parcellations. As an example, we
compared GeoSP parcellation with Desikan-Killiany and Destrieux atlases in 50
subjects, obtaining more homogeneous parcels for GeoSP and minor differences in
structural connectivity reproducibility across subjects.

GeoSP 是一种用于皮层分区的并行方法，基于测地距离创建皮层网格的分区，考虑了回缩脑回和沟回的拓扑结构。该方法通过图表征网格并在并行模式下执行 K-means 聚类，此外还有两种使用模式。结果表明，该方法对于两种模式，不同数量的子分区均表现出同质子分区，且执行时间较短。与 Desikan-Killiany 和 Destrieux 图集相比，GeoSP 分区的均匀性更好，并且跨受试者的结构连接再现性差异较小。

GeoSP: 基于测地距离的大脑皮层表面划分的并行方法

GeoSP: A parallel method for a cortical surface parcellation based on  geodesic distance

Traditional neuroimage analysis pipelines involve computationally intensive,
time-consuming optimization steps, and thus, do not scale well to large cohort
studies with thousands or tens of thousands of individuals. In this work we
propose a fast and accurate deep learning based neuroimaging pipeline for the
automated processing of structural human brain MRI scans, replicating
FreeSurfer's anatomical segmentation including surface reconstruction and
cortical parcellation. To this end, we introduce an advanced deep learning
architecture capable of whole brain segmentation into 95 classes. The network
architecture incorporates local and global competition via competitive dense
blocks and competitive skip pathways, as well as multi-slice information
aggregation that specifically tailor network performance towards accurate
segmentation of both cortical and sub-cortical structures. Further, we perform
fast cortical surface reconstruction and thickness analysis by introducing a
spectral spherical embedding and by directly mapping the cortical labels from
the image to the surface. This approach provides a full FreeSurfer alternative
for volumetric analysis (in under 1 minute) and surface-based thickness
analysis (within only around 1h runtime). For sustainability of this approach
we perform extensive validation: we assert high segmentation accuracy on
several unseen datasets, measure generalizability and demonstrate increased
test-retest reliability, and high sensitivity to group differences in dementia.

本研究基于深度学习提出了一种快速准确的神经影像处理流程，用于自动处理结构人脑 MRI 扫描。我们在 95 个类别中引入多元切片信息聚合，利用竞态稠密块和竞态跳跃路径实现局部和全局竞争，并通过将大脑标签直接映射到表面实现了皮质表面建模和厚度分析。在多个数据集中进行了广泛的验证，证明了该方法的高精度、高可靠性和高灵敏性。

FastSurfer -- 快速准确的基于深度学习的神经影像处理管道

FastSurfer -- A fast and accurate deep learning based neuroimaging  pipeline

In this thesis, we present robust and fully-automated methods for the
subdivision of the entire human cerebral cortex based on connectivity
information. Our contributions are four-fold: First, we propose a clustering
approach to delineate a cortical parcellation that provides a reliable
abstraction of the brain's functional organisation. Second, we cast the
parcellation problem as a feature reduction problem and make use of manifold
learning and image segmentation techniques to identify cortical regions with
distinct structural connectivity patterns. Third, we present a multi-layer
graphical model that combines within- and between-subject connectivity, which
is then decomposed into a cortical parcellation that can represent the whole
population, while accounting for the variability across subjects. Finally, we
conduct a large-scale, systematic comparison of existing parcellation methods,
with a focus on providing some insight into the reliability of brain
parcellations in terms of reflecting the underlying connectivity, as well as,
revealing their impact on network analysis.
We evaluate the proposed parcellation methods on publicly available data from
the Human Connectome Project and a plethora of quantitative and qualitative
evaluation techniques investigated in the literature. Experiments across
multiple resolutions demonstrate the accuracy of the presented methods at both
subject and group levels with regards to reproducibility and fidelity to the
data. The neuro-biological interpretation of the proposed parcellations is also
investigated by comparing parcel boundaries with well-structured properties of
the cerebral cortex. Results show the advantage of connectivity-driven
parcellations over traditional approaches in terms of better fitting the
underlying connectivity.

该论文针对连接信息，提出了无损整合整个人类大脑皮层的划分方法，利用聚类和流形学习技术、图形模型等手段，实现了对大脑结构和功能特征的准确提取，并对其进行了多层次评估。