Deep learning trends for focal brain pathology segmentation in MRI

TL;DR

This paper surveys the recent advancements of deep learning, especially CNNs, in segmenting focal brain pathologies in MRI, highlighting their unique features and configurations for medical image analysis.

Contribution

It provides a comprehensive overview of CNN-based methods for brain pathology segmentation, emphasizing their specific adaptations and differences from traditional machine learning.

Findings

Deep learning methods, particularly CNNs, have achieved promising results in brain pathology segmentation.

CNNs require specific configurations tailored to medical imaging characteristics.

The survey highlights the intrinsic differences between deep learning and other machine learning approaches.

Abstract

Segmentation of focal (localized) brain pathologies such as brain tumors and brain lesions caused by multiple sclerosis and ischemic strokes are necessary for medical diagnosis, surgical planning and disease development as well as other applications such as tractography. Over the years, attempts have been made to automate this process for both clinical and research reasons. In this regard, machine learning methods have long been a focus of attention. Over the past two years, the medical imaging field has seen a rise in the use of a particular branch of machine learning commonly known as deep learning. In the non-medical computer vision world, deep learning based methods have obtained state-of-the-art results on many datasets. Recent studies in computer aided diagnostics have shown deep learning methods (and especially convolutional neural networks - CNN) to yield promising results. In this chapter, we provide a survey of CNN methods applied to medical imaging with a focus on brain pathology segmentation. In particular, we discuss their characteristic peculiarities and their specific configuration and adjustments that are best suited to segment medical images. We also underline the intrinsic differences deep learning methods have with other machine learning methods.