Reconstructing the cortex from longitudinal MRI is indispensable for
analyzing morphological changes in the human brain. Despite the recent
disruption of cortical surface reconstruction with deep learning, challenges
arising from longitudinal data are still persistent. Especially the lack of
strong spatiotemporal point correspondence hinders downstream analyses due to
the introduced noise. To address this issue, we present V2C-Long, the first
dedicated deep learning-based cortex reconstruction method for longitudinal
MRI. In contrast to existing methods, V2C-Long surfaces are directly comparable
in a cross-sectional and longitudinal manner. We establish strong inherent
spatiotemporal correspondences via a novel composition of two deep mesh
deformation networks and fast aggregation of feature-enhanced within-subject
templates. The results on internal and external test data demonstrate that
V2C-Long yields cortical surfaces with improved accuracy and consistency
compared to previous methods. Finally, this improvement manifests in higher
sensitivity to regional cortical atrophy in Alzheimer's disease.

使用深度学习方法的 V2C-Long 重建了长期 MRI 上的皮层表面，并且通过建立强大的时空对应关系，提高了对阿尔茨海默病中区域性皮质萎缩的敏感性。

V2C-Long：带时空对应的纵向皮层重建

V2C-Long: Longitudinal Cortex Reconstruction with Spatiotemporal  Correspondence

We address the problem of unsupervised semantic segmentation of outdoor LiDAR
point clouds in diverse traffic scenarios. The key idea is to leverage the
spatiotemporal nature of a dynamic point cloud sequence and introduce
drastically stronger augmentation by establishing spatiotemporal
correspondences across multiple frames. We dovetail clustering and pseudo-label
learning in this work. Essentially, we alternate between clustering points into
semantic groups and optimizing models using point-wise pseudo-spatiotemporal
labels with a simple learning objective. Therefore, our method can learn
discriminative features in an unsupervised learning fashion. We show promising
segmentation performance on Semantic-KITTI, SemanticPOSS, and FLORIDA benchmark
datasets covering scenarios in autonomous vehicle and intersection
infrastructure, which is competitive when compared against many existing fully
supervised learning methods. This general framework can lead to a unified
representation learning approach for LiDAR point clouds incorporating domain
knowledge.

我们解决了在各种交通场景下，对室外 LiDAR 点云进行无监督语义分割的问题，通过建立多帧之间的时空对应关系，将点云序列的时空特征引入强化扩充，融合聚类和伪标签学习，以无监督的学习方式学习辨别特征，展现出在自动驾驶车辆和交叉口基础设施方面的分割性能在 Semantic-KITTI，SemanticPOSS 和 FLORIDA 基准数据集上表现得很有竞争力，该通用框架可以为 LiDAR 点云的统一表示学习方法融入领域知识。