Here we present a method for the simultaneous segmentation of white matter
lesions and normal-appearing neuroanatomical structures from multi-contrast
brain MRI scans of multiple sclerosis patients. The method integrates a novel
model for white matter lesions into a previously validated generative model for
whole-brain segmentation. By using separate models for the shape of anatomical
structures and their appearance in MRI, the algorithm can adapt to data
acquired with different scanners and imaging protocols without retraining. We
validate the method using four disparate datasets, showing robust performance
in white matter lesion segmentation while simultaneously segmenting dozens of
other brain structures. We further demonstrate that the contrast-adaptive
method can also be safely applied to MRI scans of healthy controls, and
replicate previously documented atrophy patterns in deep gray matter structures
in MS. The algorithm is publicly available as part of the open-source
neuroimaging package FreeSurfer.

提出了一种对多发性硬化患者进行多序列脑 MRI 扫描的白质病变和正常神经解剖结构同时分割的方法，并通过四个不同的数据集验证了该方法的高鲁棒性。

多发性硬化全脑与病变同时分割对比适应方法

A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion  Segmentation in Multiple Sclerosis

Deep convolutional neural networks are powerful tools for learning visual
representations from images. However, designing efficient deep architectures to
analyse volumetric medical images remains challenging. This work investigates
efficient and flexible elements of modern convolutional networks such as
dilated convolution and residual connection. With these essential building
blocks, we propose a high-resolution, compact convolutional network for
volumetric image segmentation. To illustrate its efficiency of learning 3D
representation from large-scale image data, the proposed network is validated
with the challenging task of parcellating 155 neuroanatomical structures from
brain MR images. Our experiments show that the proposed network architecture
compares favourably with state-of-the-art volumetric segmentation networks
while being an order of magnitude more compact. We consider the brain
parcellation task as a pretext task for volumetric image segmentation; our
trained network potentially provides a good starting point for transfer
learning. Additionally, we show the feasibility of voxel-level uncertainty
estimation using a sampling approximation through dropout.

通过采用扩张卷积和残差连接等现代卷积神经网络的高效和灵活的元素，该研究提出了一种高分辨率、紧凑的卷积神经网络，用于分割体积图像，并证明了其比现有的体积分割网络更紧凑，能够对大规模图像数据进行 3D 表示的学习，应用于神经解剖学结构的分割。同时也展示了通过 dropout 实现体素级不确定性估计的可行性。