Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

Abstract

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.

Keywords: Brain lesion segmentation; Convolutional neural network; Deep learning; Quantitative brain MRI.

Fig. 1

A schematic representation of a convolutional neural network (CNN) training process

Fig. 2

Schematic illustration of a patch-wise CNN architecture for brain tumor segmentation task

Fig. 3

Schematic illustration of a semantic-wise CNN architecture for brain tumor segmentation task

Fig. 4

Schematic illustration of a cascaded CNN architecture for brain tumor segmentation task, where the output of the first network (CNN 1) is used in addition to image data for a refined input to the second network (CNN 2), which provides final segmentation