AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis

Schlaeger, Sarah; Shit, Suprosanna; Eichinger, Paul; Hamann, Marco; Opfer, Roland; Krüger, Julia; Dieckmeyer, Michael; Schön, Simon; Mühlau, Mark; Zimmer, Claus; Kirschke, Jan S.; Wiestler, Benedikt; Hedderich, Dennis M.

doi:10.1186/s13244-023-01460-3

Cited by 4 publications

(2 citation statements)

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“…A number of automatic and semi-automatic techniques for identifying and segmenting enhancing lesions in MS have been reported [ 5 , 10 , 16 , 17 , 18 ]. As described by Coronado et al [ 17 ], these methods have limitations that include the need for specialized MRI pulse sequence, extensive pre-processing, minimizing false lesion classification etc.…”

Section: Introductionmentioning

confidence: 99%

“…As described by Coronado et al [ 17 ], these methods have limitations that include the need for specialized MRI pulse sequence, extensive pre-processing, minimizing false lesion classification etc. Therefore, more recently, deep learning was used to delineate enhancing lesions in MS [ 16 , 17 , 19 ]. These DL methods are mainly based on convolutional neural networks (CNNs).…”

Section: Introductionmentioning

confidence: 99%

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Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning

Uwaeze,

Narayana,

Kamali

et al. 2024

Diagnostics

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Background: Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration of GBCA results in the accumulation of Gd in tissues. In addition, GBCA administration increases health care costs. Thus, reducing or eliminating GBCA administration for active lesion detection is important for improved patient safety and reduced healthcare costs. Current state-of-the-art methods for identifying active lesions in brain MRI without GBCA administration utilize data-intensive deep learning methods. Objective: To implement nonlinear dimensionality reduction (NLDR) methods, locally linear embedding (LLE) and isometric feature mapping (Isomap), which are less data-intensive, for automatically identifying active lesions on brain MRI in MS patients, without the administration of contrast agents. Materials and Methods: Fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images were included in the multiparametric MRI dataset used in this study. Subtracted pre- and post-contrast T1-weighted images were labeled by experts as active lesions (ground truth). Unsupervised methods, LLE and Isomap, were used to reconstruct multiparametric brain MR images into a single embedded image. Active lesions were identified on the embedded images and compared with ground truth lesions. The performance of NLDR methods was evaluated by calculating the Dice similarity (DS) index between the observed and identified active lesions in embedded images. Results: LLE and Isomap, were applied to 40 MS patients, achieving median DS scores of 0.74 ± 0.1 and 0.78 ± 0.09, respectively, outperforming current state-of-the-art methods. Conclusions: NLDR methods, Isomap and LLE, are viable options for the identification of active MS lesions on non-contrast images, and potentially could be used as a clinical decision tool.

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