Level set evolution with intensity prior knowledge for multiple sclerosis lesion segmentation

Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches

Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach

We present the application of limited one-time sampling irregularity map (LOTS-IM): a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), for quantitatively assessing white matter hyperintensities (WMH) of presumed vascular origin, and multiple sclerosis (MS) lesions and their progression. LOTS-IM generates an irregularity map (IM) that represents all voxels as irregularity values with respect to the ones considered ”normal”. Unlike probability values, IM represents both regular and irregular regions in the brain based on the original MRI's texture information. We evaluated and compared the use of IM for WMH and MS lesions segmentation on T2-FLAIR MRI with the state-of-the-art unsupervised lesions’ segmentation method, Lesion Growth Algorithm from the public toolbox Lesion Segmentation Toolbox (LST-LGA), with several well established conventional supervised machine learning schemes and with state-of-the-art supervised deep learning methods for WMH segmentation. In our experiments, LOTS-IM outperformed unsupervised method LST-LGA on WMH segmentation, both in performance and processing speed, thanks to the limited one-time sampling scheme and its implementation on GPU. Our method also outperformed supervised conventional machine learning algorithms (i.e., support vector machine (SVM) and random forest (RF)) and deep learning algorithms (i.e., deep Boltzmann machine (DBM) and convolutional encoder network (CEN)), while yielding comparable results to the convolutional neural network schemes that rank top of the algorithms developed up to date for this purpose (i.e., UResNet and UNet). LOTS-IM also performed well on MS lesions segmentation, performing similar to LST-LGA. On the other hand, the high sensitivity of IM on depicting signal change deems suitable for assessing MS progression, although care must be taken with signal changes not reflective of a true pathology.

Background: Multiple Sclerosis (MS) diagnosis and evaluation is often a challenging task due to its growing need for multimodal MRI acquisition protocol. Recently, the scientific community offers several computational alternatives to the time-consuming and subjective task of manual MS lesion segmentation. Although there is an increasing number of MS lesion segmentation methods, a controlled and realistic simulation environment can benefit the community for a reliable evaluation procedure. Methods: This study proposes an automatic parametric MS lesion simulation framework (MS-MIST) with the objective to emulate real MS-like pattern on MRI data of healthy individuals. The voxel gray-level patterns, spatial location, and shapes extracted from MS patient allow consistent simulation features. We used both visual evaluation from an expert radiologist in the field of MS diagnosis and SPM Lesion Segmentation Tool (LST) for qualitative and quantitative simulation quality, respectively. Results: Our results show that both the agreement between the automatic segmentation with the simulated lesions (Pearson’s correlation R = 0.977) and the segmentation quality scores, i.e., sensitivity (mean = 0.9050), specificity (mean = 0.9992), dice similarity (mean = 0.8972), and accuracy (mean = 0.9984), are consistent between MS-MIST and real clinical settings. Conclusions: MS-MIST proposes a practical solution to common issues discussed in the literature, such as inconsistency with real lesions geometry, imprecise lesion spatial and signal variability, lack of multiple MRI modalities and restrictions to simulate different lesion loads. It is worth noting that the simulation platform is freely available as an open-source code to the community.

icobrain ms 5.1: Combining unsupervised and supervised approaches for improving the detection of multiple sclerosis lesions

The Segmentation is a fundamental technique used in image processing to extract suspicious regions from a given image. Quantitative assessment of Magnetic Resonance Imaging (MRI) lesion load of patients with multiple sclerosis (MS) is the most objective approach for a better understanding of the history of the pathology, either natural or modified by therapies. Recently, numerous methods have been introduced for the segmentation of MS lesions in MR images. In this work, we propose to segment the images based on a constrained Gaussian Mixture Model (GMM) and using Genetic Algorithm to determine the optimal parameters of this highly non-linear model. We use this method for automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) of magnetic resonance images. Moreover, a preprocessing step is also performed to suppress artifacts and to remove the unwanted skull portions from brain MRI. The proposed method has been tested on real patients' MR images. The experimental results show that the proposed method can effectively segment MS lesions in the brain MR images.

Lesion load (LL), deep gray matter (DGM) and normal-appearing white matter (NAWM) susceptibility and morphometry may help in monitoring brain changes in multiple sclerosis (MS) patients. We aimed at evaluating the feasibility of a fully automated segmentation and the potential interrelation between these biomarkers and clinical disability. Sixty-six patients with brain MRIs and clinical evaluations (Expanded Disability Status Scale [EDSS]) were retrospectively included. Automated prototypes were used for the segmentation and morphometry of brain regions (MorphoBox) and MS lesions (LeManPV). Susceptibility maps were estimated using standard post-processing (RESHARP and TVSB). Spearman's rho was computed to evaluate the interrelation between biomarkers and EDSS. We found (i) anticorrelations between the LL and right thalamus susceptibility (rho = -0.46, p < 0.001) and between the LL and NAWM susceptibility (rho = [-0.68 to -0.25], p ≤ 0.05); (ii) an anticorrelation between LL and DGM (rho = [-0.71 to -0.36], p < 0.04) and WM morphometry (rho = [-0.64 to -0.28], p ≤ 0.01); and (iii) a positive correlation between EDSS and LL (rho = [0.28 to 0.5], p ≤ 0.03) and anticorrelation between EDSS and NAWM susceptibility (rho = [-0.29 to -0.38], p < 0.014). Fully automated brain morphometry and susceptibility monitoring is feasible in MS patients. The lesion load, thalamus and NAWM susceptibility values and trophicity are interrelated and correlate with disability.

ABSTRACTBackground and PurposeAssessment of brain lesions on magnetic resonance imaging (MRI) is crucial for research in multiple sclerosis (MS). Manual segmentation is time‐consuming and inconsistent. We aimed to develop an automated MS lesion segmentation algorithm for T2‐weighted fluid‐attenuated inversion recovery (FLAIR) MRI.MethodsWe developed FLAIR Lesion Analysis in Multiple Sclerosis (FLAMeS), a deep learning‐based MS lesion segmentation algorithm based on the nnU‐Net 3D full‐resolution U‐Net and trained on 668 FLAIR 1.5 and 3 tesla scans from persons with MS. FLAMeS was evaluated on three external datasets: MSSEG‐2 (n = 14), MSLesSeg (n = 51), and a clinical cohort (n = 10), and compared to SAMSEG, LST‐LPA, and LST‐AI. Performance was assessed qualitatively by two blinded experts and quantitatively by comparing automated and ground truth lesion masks using standard segmentation metrics.ResultsIn a blinded qualitative review of 20 scans, both raters selected FLAMeS as the most accurate segmentation in 15 cases, with one rater favoring FLAMeS in two additional cases. Across all testing datasets, FLAMeS achieved a mean Dice score of 0.74, a true positive rate of 0.84, and an F1 score of 0.78, consistently outperforming the benchmark methods. For other metrics, including positive predictive value, relative volume difference, and false positive rate, FLAMeS performed similarly to or better than benchmark methods. Most lesions missed by FLAMeS were smaller than 10 mm3, whereas the benchmark methods missed larger lesions in addition to smaller ones.ConclusionsFLAMeS is an accurate, robust method for MS lesion segmentation that outperforms other publicly available methods.

To summarize recent advancements in artificial intelligence-driven lesion segmentation and novel neuroimaging modalities that enhance the identification and characterization of multiple sclerosis (MS) lesions, emphasizing their implications for clinical use and research. Artificial intelligence, particularly deep learning approaches, are revolutionizing MS lesion assessment and segmentation, improving accuracy, reproducibility, and efficiency. Artificial intelligence-based tools now enable automated detection not only of T2-hyperintense white matter lesions, but also of specific lesion subtypes, including gadolinium-enhancing, central vein sign-positive, paramagnetic rim, cortical, and spinal cord lesions, which hold diagnostic and prognostic value. Novel neuroimaging techniques such as quantitative susceptibility mapping (QSM), χ-separation imaging, and soma and neurite density imaging (SANDI), together with PET, are providing deeper insights into lesion pathology, better disentangling their heterogeneities and clinical relevance. Artificial intelligence-powered lesion segmentation tools hold great potential for improving fast, accurate and reproducible lesional assessment in the clinical scenario, thus improving MS diagnosis, monitoring, and treatment response assessment. Emerging neuroimaging modalities may contribute to advance the understanding MS pathophysiology, provide more specific markers of disease progression, and novel potential therapeutic targets.

This paper presents MSLesSeg, a new, publicly accessible MRI dataset designed to advance research in Multiple Sclerosis (MS) lesion segmentation. The dataset comprises 115 scans of 75 patients including T1, T2 and FLAIR sequences, along with supplementary clinical data collected across different sources. Expert-validated annotations provide high-quality lesion segmentation labels, establishing a reliable human-labeled dataset for benchmarking. Part of the dataset was shared with expert scientists with the aim to compare the last automatic AI-based image segmentation solutions with an expert-biased handmade segmentation. In addition, an AI-based lesion segmentation of MSLesSeg was developed and technically validated against the last state-of-the-art methods. The dataset, the detailed analysis of researcher contributions, and the baseline results presented here mark a significant milestone for advancing automated MS lesion segmentation research.

Should Spinal MRI Be Routinely Performed in Patients With Clinically Isolated Optic Neuritis?

The data presented in this article is related to the research article entitled “Longitudinal multiple sclerosis lesion segmentation: Resource and challenge” (Carass et al., 2017) [1]. In conjunction with the 2015 International Symposium on Biomedical Imaging, we organized a longitudinal multiple sclerosis (MS) lesion segmentation challenge providing training and test data to registered participants. The training data consists of five subjects with a mean of 4.4 (±0.55) time-points, and test data of fourteen subjects with a mean of 4.4 (±0.67) time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. The training data including multi-modal scans and manually delineated lesion masks is available for download.11The data and evaluation website is: http://smart-stats-tools.org/lesion-challenge-2015. In addition, the testing data is also being made available in conjunction with a website for evaluating the automated analysis of the testing data.

Multiple sclerosis lesions segmentation is an important step in the diagnosis and tracking in the evolution of the disease. Convolutional Neural Networks (CNN) have been obtaining successful results in the task of lesion segmentation in recent years, but still present problem segmenting boundaries of the lesions. In this work we focus the learning process on hard voxels close to the boundaries of the lesions by means of a stratified sampling and the use of focal loss function that dynamically increase the penalization on this kind of voxels. This approach was applied on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset (ISBI2015 (https://smart-stats-tools.org/lesion-challenge)), obtaining better results than approaches using binary cross entropy loss and focal loss functions with uniform sampling.

Multiple sclerosis (MS) one of the chronic Idiopathic disease needs a precise segmentation method to analyze the MS White Matter Lesion (WML) variation with respect the Extended Disability scale (EDSS) value of MS MR brain images. In this work, we use the Active contour segmentation method to segment the MS brain tissue from T2W axial view MS preprocessed MR images. The Active contour segmentation to segment MS affected area using the ITK-SNAP 3D segmentation tool. The segmented MS WML lesion image region to measure the MS lesion affected area volume and calculate the image shape and statistical feature value using Gray Level Co-occurrence Matrix (GLCM). The MS WML volume, shape, and statistical feature are correlated with the different EDSS value of the MS patient data set. The shape, statistical feature parameter, and lesion volume correlation study are very useful to monitoring changes associated with improvement of disability stage from 1–9 category of MS progression. Reproducibility and sensitivity must be enhanced to use the analysis for consecutive monitoring in individuals.

In this work we present BIANCA‐MS, a novel tool for brain white matter lesion segmentation in multiple sclerosis (MS), able to generalize across both the wide spectrum of MRI acquisition protocols and the heterogeneity of manually labeled data. BIANCA‐MS is based on the original version of BIANCA and implements two innovative elements: a harmonized setting, tested under different MRI protocols, which avoids the need to further tune algorithm parameters to each dataset; and a cleaning step developed to improve consistency in automated and manual segmentations, thus reducing unwanted variability in output segmentations and validation data. BIANCA‐MS was tested on three datasets, acquired with different MRI protocols. First, we compared BIANCA‐MS to other widely used tools. Second, we tested how BIANCA‐MS performs in separate datasets. Finally, we evaluated BIANCA‐MS performance on a pooled dataset where all MRI data were merged. We calculated the overlap using the DICE spatial similarity index (SI) as well as the number of false positive/negative clusters (nFPC/nFNC) in comparison to the manual masks processed with the cleaning step. BIANCA‐MS clearly outperformed other available tools in both high‐ and low‐resolution images and provided comparable performance across different scanning protocols, sets of modalities and image resolutions. BIANCA‐MS performance on the pooled dataset (SI: 0.72 ± 0.25, nFPC: 13 ± 11, nFNC: 4 ± 8) were comparable to those achieved on each individual dataset (median across datasets SI: 0.72 ± 0.28, nFPC: 14 ± 11, nFNC: 4 ± 8). Our findings suggest that BIANCA‐MS is a robust and accurate approach for automated MS lesion segmentation.