Validation of White-Matter Lesion Change Detection Methods on a Novel Publicly Available MRI Image Database

Abstract

Changes of white-matter lesions (WMLs) are good predictors of the progression of neurodegenerative diseases like multiple sclerosis (MS). Based on longitudinal magnetic resonance (MR) imaging the changes can be monitored, while the need for their accurate and reliable quantification led to the development of several automated MR image analysis methods. However, an objective comparison of the methods is difficult, because publicly unavailable validation datasets with ground truth and different sets of performance metrics were used. In this study, we acquired longitudinal MR datasets of 20 MS patients, in which brain regions were extracted, spatially aligned and intensity normalized. Two expert raters then delineated and jointly revised the WML changes on subtracted baseline and follow-up MR images to obtain ground truth WML segmentations. The main contribution of this paper is an objective, quantitative and systematic evaluation of two unsupervised and one supervised intensity based change detection method on the publicly available datasets with ground truth segmentations, using common pre- and post-processing steps and common evaluation metrics. Besides, different combinations of the two main steps of the studied change detection methods, i.e. dissimilarity map construction and its segmentation, were tested to identify the best performing combination.

Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks

“…Among other methods, image differences ( Battagliniet al, 2014 , Ganileret al, 2014 ) and deformation fields ( Boscet al, 2003 , Cabezaset al, 2016 , Salemet al, 2017 ) have been used to detect new lesions. Also, intensity-based approaches using local context between scans have been proposed ( Lesjak et al, 2016 ). Overall, methods for detection of lesion growth have largely relied on classic image processing methods so far ( Cheng et al, 2018 , Schmidt et al, 2019 ).…”

“…However, this approach is associated with high variability and inconsistency ( García-Lorenzo et al, 2013 ). Others have tried to explicitly incorporate information from both MRI volumes, e. g., intensity-based approaches considering local context between volumes have shown promising results ( Lesjak et al, 2016 ). Overall, longitudinal methods have relied primarily on classical image processing methods until today ( Cheng et al, 2018 ).…”

Change Detection: The Framework of Visual Inspection System for Railway Plug Defects

“…However, for video surveillance, the Gaussian mixture model was used for static scene recovery [21]. Meanwhile, for remote sensing and medical images, to perform image alignment, some elastic registration algorithms should be used to deal with the deformed images [20,22] Our study is the first practical system for plug inspection, and so there are hardly any images containing defective plugs collected by us. Therefore, just at the present stage, the number of defective plugs is too scarce to train a classifier that can classify defective plugs directly from large numbers of inspection images.…”

“…To solve those problems with plug inspection, we design a VIS using a change detection framework. Although the concept of change detection is already used in the fields of remote sensing [19,20], video surveillance [21], and medical diagnosis and treatment [22], the change detection framework proposed in this paper is designed especially for railways. Because the detected objects vary based on the application, it is difficult to compare algorithms using the change detection framework [23].…”

Joint Registration and Change Detection in Longitudinal Brain MRI

“…2. Comparison of the registration and lesion detection of sequential-def and joint on real data [21]. The second column shows I 2 registered on I 1 and the third column shows the lesion map superimposed on I 2 .…”