  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.
