DeepMRSeg: A convolutional deep neural network for anatomy and abnormality segmentation on MR images
This work addresses the need for a versatile segmentation tool in neuroimaging, offering a method that can be applied to various tasks with provided code and models, though it is incremental as it builds on existing UNet architectures.
The authors tackled the problem of generic segmentation of brain anatomy and abnormalities on minimally processed raw MRI scans, achieving high accuracy across multiple tasks including white matter lesion, deep brain structures, and hippocampus segmentation.
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.