A major challenge in stroke research and stroke recovery predictions is the
determination of a stroke lesion's extent and its impact on relevant brain
systems. Manual segmentation of stroke lesions from 3D magnetic resonance (MR)
imaging volumes, the current gold standard, is not only very time-consuming,
but its accuracy highly depends on the operator's experience. As a result,
there is a need for a fully automated segmentation method that can efficiently
and objectively measure lesion extent and the impact of each lesion to predict
impairment and recovery potential which might be beneficial for clinical,
translational, and research settings. We have implemented and tested a fully
automatic method for stroke lesion segmentation which was developed using eight
different 2D-model architectures trained via transfer learning (TL) and mixed
data approaches. Additionally, the final prediction was made using a novel
ensemble method involving stacking and agreement window. Our novel method was
evaluated in a novel in-house dataset containing 22 T1w brain MR images, which
were challenging in various perspectives, but mostly because they included T1w
MR images from the subacute (which typically less well defined T1 lesions) and
chronic stroke phase (which typically means well defined T1-lesions).
Cross-validation results indicate that our new method can efficiently and
automatically segment lesions fast and with high accuracy compared to ground
truth. In addition to segmentation, we provide lesion volume and weighted
lesion load of relevant brain systems based on the lesions' overlap with a
canonical structural motor system that stretches from the cortical motor region
to the lowest end of the brain stem.

本文提出了一种基于深度学习技术的脑卒中损伤分割方法，旨在实现损伤扩展和影响的客观测量，以预测神经功能损害和康复潜力。该方法可以高效、自动地进行分割，并同时提供损伤体积和对大脑结构的加权损伤量量化。

基于迁移学习的脑部损伤分割元分析

Meta-Analysis of Transfer Learning for Segmentation of Brain Lesions

We explore an ensembled $\Sigma$-net for fast parallel MR imaging, including
parallel coil networks, which perform implicit coil weighting, and sensitivity
networks, involving explicit sensitivity maps. The networks in $\Sigma$-net are
trained in a supervised way, including content and GAN losses, and with various
ways of data consistency, i.e., proximal mappings, gradient descent and
variable splitting. A semi-supervised finetuning scheme allows us to adapt to
the k-space data at test time, which, however, decreases the quantitative
metrics, although generating the visually most textured and sharp images. For
this challenge, we focused on robust and high SSIM scores, which we achieved by
ensembling all models to a $\Sigma$-net.

探索使用 Sigma-net 进行快速并行 MR 成像的方法，其中包括并行线圈网络、敏感度网络以及数据相容性等，通过 Sigma-net 的方法最大化 SSIM 评分。

Σ-Net：集成迭代深度神经网络加速并行 MR 图像重建

$Σ$-net: Ensembled Iterative Deep Neural Networks for Accelerated  Parallel MR Image Reconstruction

Parallel imaging has been an essential technique to accelerate MR imaging.
Nevertheless, the acceleration rate is still limited due to the ill-condition
and challenges associated with the undersampled reconstruction. In this paper,
we propose a model-based convolutional de-aliasing network with adaptive
parameter learning to achieve accurate reconstruction from multi-coil
undersampled k-space data. Three main contributions have been made: a
de-aliasing reconstruction model was proposed to accelerate parallel MR imaging
with deep learning exploring both spatial redundancy and multi-coil
correlations; a split Bregman iteration algorithm was developed to solve the
model efficiently; and unlike most existing parallel imaging methods which rely
on the accuracy of the estimated multi-coil sensitivity, the proposed method
can perform parallel reconstruction from undersampled data without explicit
sensitivity calculation. Evaluations were conducted on \emph{in vivo} brain
dataset with a variety of undersampling patterns and different acceleration
factors. Our results demonstrated that this method could achieve superior
performance in both quantitative and qualitative analysis, compared to three
state-of-the-art methods.

本文提出了一种基于模型卷积去混叠神经网络的方法，使用自适应参数学习从多线圈下采样 k 空间数据实现精确重建，以加速 MR 成像。该方法不需要估计多线圈灵敏度，评估结果表明其在定量和定性分析方面比目前现有的三种最新方法均表现出更优异的性能