DeepMRSeg：用于MR图像解剖和异常分割的卷积深度神经网络
DeepMRSeg: A convolutional deep neural network for anatomy and abnormality segmentation on MR images
摘要：
分割一直是神经影像学中的主要任务。已经开发了大量的自动化方法用于分割健康和患病的脑组织。近年来，深度学习技术因其在不同分割问题中的高准确性而引起了广泛关注。我们提出了一种新的基于深度学习的分割方法DeepMRSeg，它可以以通用的方式应用于各种分割任务。该架构结合了生物医学图像分割和计算机视觉领域的最新进展。我们使用修改后的UNet架构，利用多个卷积滤波器的大小，以实现多尺度特征提取自适应所需的分割任务。重要的是，我们的方法对最低限度处理的原始MRI扫描进行操作。我们在广泛的分割任务上验证了我们的方法，包括白质病变分割、脑深部结构分割和海马体分割。我们提供代码和预先训练的模型，让研究人员在自己的数据集上应用我们的方法。
英文摘要：
Segmentation has been a major task in neuroimaging. A large number of automated methods have been developed for segmenting healthy and diseased brain tissues. In recent years, deep learning techniques have attracted a lot of attention as a result of their high accuracy in different segmentation problems. We present a new deep learning based segmentation method, DeepMRSeg, that can be applied in a generic way to a variety of segmentation tasks. The proposed architecture combines recent advances in the field of biomedical image segmentation and computer vision. We use a modified UNet architecture that takes advantage of multiple convolution filter sizes to achieve multi-scale feature extraction adaptive to the desired segmentation task. Importantly, our method operates on minimally processed raw MRI scan. We validated our method on a wide range of segmentation tasks, including white matter lesion segmentation, segmentation of deep brain structures and hippocampus segmentation. We provide code and pre-trained models to allow researchers apply our method on their own datasets.
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