MRI Images, Brain Lesions and Deep Learning

TL;DR

This paper reviews deep learning methods for classifying and segmenting brain lesions in MRI images, highlighting recent advances, challenges with dataset size, and the need for multidisciplinary collaboration for clinical application.

Contribution

It provides a comprehensive review of deep learning models for brain lesion detection in MRI, emphasizing the growth, performance metrics, and practical limitations of current approaches.

Findings

Deep learning models achieve high accuracy with Dice scores up to 0.99.

Most models use small datasets, limiting clinical applicability.

Multidisciplinary efforts are needed to translate research into practice.

Abstract

Medical brain image analysis is a necessary step in Computer Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of White Matter Hyperintensities (WMHs) of brain MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out of a total of 140 documents we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and proposal of new models of deep learning to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however with little practical application due to the uses of small datasets and lack of reproducibility. Therefore, the main conclusion is to establish multidisciplinary research groups to overcome the gap between CAD developments and their complete utilization in the clinical environment.