Reinforcement Learning-Enhanced Attention U-Net for Cervical Spinal Cord and Multiple Sclerosis Lesion Segmentation

The impact of cervical spinal cord atrophy on quality of life in multiple sclerosis

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 and Objectives:Multiple sclerosis (MS) lesion segmentation is important in estimating the progress of the disease and measuring the impact of new clinical treatments. Manual lesion delineation for the segmentation of lesions is time-consuming and suffers from observer variability. Therefore, a fully automated MS lesion segmentation method is considerable important in clinical practice.Subjects and Methods:In this study, we present a multilabel fusion embedded level set method for white matter lesion segmentation from MS patient images. Specifically, we focus on the validation of the variational level set method. Lesion segmentation is achieved by extending the level set contour which consists of an intensity-constrained term, an image data term, and a regularization term.Results:To compare the performance of our method with other state-of-the-art methods, we evaluated the methods with 25 magnetic resonance imaging datasets of MS patients. The dice score reaches an average of 0.55 for the proposed method. The sensitivity value and specificity value reach an average of 0.89 and 0.14, respectively.Conclusions:Experimental results demonstrate that our method is robust to parameter setting and outperforms other methods. The intensity-constrained term plays a key role in improving the segmentation accuracy. The experimental results show that our approach is effective and robust for lesion segmentation, which might simplify the quantification of lesions in basic research and even clinical trials.

Purpose: Multiple Sclerosis (MS) is an acute, autoimmune, and inflammatory disease in the central nervous system. This study investigated the effect of sagittal Short Tau Inversion Recovery (STIR) and T2-W Fluid Attenuated Inversion Recovery (FLAIR) sequences rather than sagittal T2-W as complementary sequences in patients with cervical spinal cord lesions and suspected MS.
 Materials and Methods: This cross-sectional study was performed on all individuals referred to the Shahid Ghazi MRI center in Sanandaj for six months. Sixty patients with a cervical spine MRI request that were suspected of having MS were examined. The number of MS plaques in the sagittal T2-W FSE, sagittal STIR, and sagittal T2-W FLAIR were recorded separately. A comparison between routine sequences and sequence supplementation has been made for characterizing MS plaque in the spine.
 Results: Results showed that the greatest agreement was related to sagittal STIR, and sagittal FLAIR (Cohen’s kappa = 0.56). Whereas the least agreement values were from sagittal T2-W and sagittal FLAIR, STIR and FLAIR, T2-W and FLAIR, T2-W and STIR (Cohen’s kappa = 0.20, 0.33, 0.48, 0.55), respectively. Sagittal STIR and sagittal FLAIR were excellent predictors for MS plaques diagnosis due to the area under the ROC curve = 0.56; sensitivity (95% CI) = [0.85 (0.73426 to 0.929044)] and specificity (95% CI) = [0.46 (0.336699 to 0.600035)].
 Conclusion: Results show that FLAIR T2-W images in sagittal sequence are appropriate for detecting lesions around spinal cord lesions. Furthermore, using thresholds obtained via statistical analysis, plaques in the cervical spinal cord can be identified in sagittal STIR images.

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

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.

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.

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.

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.

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.

Depolarization and electrical stimulation enhance in vitro and in vivo sensory axon growth after spinal cord injury

This paper describes a statistical approach for segmenting multiple sclerosis lesions (tumors) from magnetic resonance imaging (MRI) images. Proposed method detects and segments the areas inside the brain that are affected by tumors. Tumor regions are the areas of higher intensity in comparison to normal tissue. Our automated method gives satisfactory results showing that the proposed method is capable of segmenting multiple sclerosis lesions of different shapes and intensities. In order to show the efficacy of proposed approach, experimental results are compared with the results of other algorithm and also with the results of manual segmentation performed by experts.

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.