DeepMRSeg: A convolutional deep neural network for anatomy and abnormality segmentation on MR images

TL;DR

DeepMRSeg is a versatile deep learning segmentation tool for MRI scans that effectively handles various neuroimaging tasks using a modified UNet architecture, operating on raw data with high accuracy.

Contribution

The paper introduces DeepMRSeg, a novel deep learning segmentation method with a multi-scale UNet architecture that works on minimally processed MRI data for diverse neuroimaging tasks.

Findings

Effective segmentation across multiple neuroimaging tasks

Operates on raw MRI scans without extensive preprocessing

Provides accessible code and pre-trained models

Abstract

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.