Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI

Abstract

In recent years, there have been multiple works of literature reviewing methods for automatically segmenting multiple sclerosis (MS) lesions. However, there is no literature systematically and individually review deep learning-based MS lesion segmentation methods. Although the previous review also included methods based on deep learning, there are some methods based on deep learning that they did not review. In addition, their review of deep learning methods did not go deep into the specific categories of Convolutional Neural Network (CNN). They only reviewed these methods in a generalized form, such as supervision strategy, input data handling strategy, etc. This paper presents a systematic review of the literature in automated multiple sclerosis lesion segmentation based on deep learning. Algorithms based on deep learning reviewed are classified into two categories through their CNN style, and their strengths and weaknesses will also be given through our investigation and analysis. We give a quantitative comparison of the methods reviewed through two metrics: Dice Similarity Coefficient (DSC) and Positive Predictive Value (PPV). Finally, the future direction of the application of deep learning in MS lesion segmentation will be discussed.

Lesion probability mapping in MS patients using a regression network on MR fingerprinting

“…After binarizing the probability maps, the dice coefficient was for the test data, which is comparable to the intra-observer variability of the manual drawer ( ) and is comparable to literature (0.47–0.95). [ 33 , 36 ] The CNN might be more robust compared with manual annotations because the network has no variations for multiple annotations. We have shown that the network only predicts lesion probability maps for the loss functions MAE and MSE.…”

“…[ 33 ] To overcome these limitations of manual segmentation, different DL architectures and networks have been used, yielding dice coefficients ranging from 0.48 to 0.95 for WM lesion segmentation. [ 33 – 36 ] Therefore, in a recent publication, it was shown that the use of regression networks for generating distance maps of the lesions might improve the WM lesion segmentation process [ 37 ]. This could provide more information about lesion geometry, structure, and changes similar to lesion probability mapping.…”

Lesion probability mapping in MS patients using a regression network on MR Fingerprinting

“…After binarizing the probability maps, the dice coefficient was 0.61 ± 0.09 for the test data, which is comparable to the intra-observer variability of the manual drawer (0.68 ± 0.23) and is comparable to literature (0.47-0.95). [33,36] The CNN might be more robust compared with manual annotations because the network has no variations for multiple annotations. We have shown that the network only predicts lesion probability maps for the loss functions MAE and MSE.…”

“…This is an information gain compared to manual annotators and compared to lesion segmentation methods based solely on the quantitative parametric maps. [36,37] In further work, this has to be compared with conventional methods for assessing these components such as the myelin or extracellular water. [43,45,46] This study has some limitations.…”

“…[33] To overcome these limitations of manual segmentation, different DL architectures and networks have been used, yielding dice coefficients ranging from 0.48 to 0.95 for WM lesion segmentation. [33][34][35][36] Therefore, in a recent publication, it was shown that this processing step can be improved by regression by also generating distance maps of the lesions. [37] This could provide more information about lesion geometry, structure, and changes similar to lesion probability mapping.…”