Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling (original) (raw)
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Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images
NeuroImage: Clinical, 2015
The location and extent of white matter lesions on magnetic resonance imaging (MRI) are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS). Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM) lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM) and the appearance (hyperintense on FLAIR) of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice) between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC) equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default parameter settings.
AJNR. American journal of neuroradiology, 2015
The accuracy of automatic tissue segmentation methods can be affected by the presence of hypointense white matter lesions during the tissue segmentation process. Our aim was to evaluate the impact of MS white matter lesions on the brain tissue measurements of 6 well-known segmentation techniques. These include straightforward techniques such as Artificial Neural Network and fuzzy C-means as well as more advanced techniques such as the Fuzzy And Noise Tolerant Adaptive Segmentation Method, fMRI of the Brain Automated Segmentation Tool, SPM5, and SPM8. Thirty T1-weighted images from patients with MS from 3 different scanners were segmented twice, first including white matter lesions and then masking the lesions before segmentation and relabeling as WM afterward. The differences in total tissue volume and tissue volume outside the lesion regions were computed between the images by using the 2 methodologies. Total gray matter volume was overestimated by all methods when lesion volume in...
Neuroradiology, 2016
Introduction Regional brain volume estimation in multiple sclerosis (MS) patients is prone to error due to white matter lesions being erroneously segmented as grey matter. The Lesion Segmentation Toolbox (LST) is an automatic tool that estimates a lesion mask based on 3D T2-FLAIR images and then uses this mask to fill the structural MRI image. The goal of this study was (1) to test the LST for estimating white matter lesion volume in a cohort of MS patients using 2D T2-FLAIR images, and (2) to evaluate the performance of the optimized LST on image segmentation and the impact on the calculated grey matter fraction (GMF). Methods The study included 110 patients with a clinically isolated syndrome and 42 with a relapsing-remitting MS scanned on a 3.0-T MRI system. In a subset of consecutively selected patients, the lesion mask was semi-manually delineated over T2-FLAIR images. After establishing the optimized LST parameters, the corresponding regional fractions were calculated for the original, filled, and masked images. Results A high agreement (intraclass correlation coefficient (ICC) = 0.955) was found between the (optimized) LST and the semi-manual lesion volume estimations. The GMF was significantly smaller when lesions were masked (mean difference −0.603, p < 0.001) or when the LST filling technique was used (mean difference −0.598, p < 0.001), compared to the GMF obtained from the original image. Conclusion LST lesion volume calculation seems reliable. GMFs are significantly reduced when a method to correct the contribution of MS lesions is used, and it may have an impact in assessing GMF differences between clinical cohorts.
A toolbox for multiple sclerosis lesion segmentation
Neuroradiology, 2015
Introduction Lesion segmentation plays an important role in the diagnosis and follow-up of multiple sclerosis (MS). This task is very time-consuming and subject to intra-and interrater variability. In this paper, we present a new tool for automated MS lesion segmentation using T1w and fluidattenuated inversion recovery (FLAIR) images. Methods Our approach is based on two main steps, initial brain tissue segmentation according to the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) performed in T1w images, followed by a second step where the lesions are segmented as outliers to the normal apparent GM brain tissue on the FLAIR image. Results The tool has been validated using data from more than 100 MS patients acquired with different scanners and at different magnetic field strengths. Quantitative evaluation provided a better performance in terms of precision while maintaining similar results on sensitivity and Dice similarity measures compared with those of other approaches. Conclusion Our tool is implemented as a publicly available SPM8/12 extension that can be used by both the medical and research communities.
A white matter lesion-filling approach to improve brain tissue volume measurements
NeuroImage: Clinical, 2014
Multiple sclerosis white matter (WM) lesions can affect brain tissue volume measurements of voxel-wise segmentation methods if these lesions are included in the segmentation process. Several authors have presented different techniques to improve brain tissue volume estimations by filling WM lesions before segmentation with intensities similar to those of WM. Here, we propose a new method to refill WM lesions, where contrary to similar approaches, lesion voxel intensities are replaced by random values of a normal distribution generated from the mean WM signal intensity of each two-dimensional slice. We test the performance of our method by estimating the deviation in tissue volume between a set of 30 T1-w 1.5 T and 30 T1-w 3 T images of healthy subjects and the same images where: WM lesions have been previously registered and afterwards replaced their voxel intensities to those between gray matter (GM) and WM tissue. Tissue volume is computed independently using FAST and SPM8. When compared with the state-of-the-art methods, on 1.5 T data our method yields the lowest deviation in WM between original and filled images, independently of the segmentation method used. It also performs the lowest differences in GM when FAST is used and equals to the best method when SPM8 is employed. On 3 T data, our method also outperforms the state-of-the-art methods when FAST is used while performs similar to the best method when SPM8 is used. The proposed technique is currently available to researchers as a stand-alone program and as an SPM extension.
Partial volume-aware assessment of multiple sclerosis lesions
NeuroImage: Clinical
White-matter lesion count and volume estimation are key to the diagnosis and monitoring of multiple sclerosis (MS). Automated MS lesion segmentation methods that have been proposed in the past 20 years reach their limits when applied to patients in early disease stages characterized by low lesion load and small lesions. We propose an algorithm to automatically assess MS lesion load (number and volume) while taking into account the mixing of healthy and lesional tissue in the image voxels due to partial volume effects. The proposed method works on 3D MPRAGE and 3D FLAIR images as obtained from current routine MS clinical protocols. The method was evaluated and compared with manual segmentation on a cohort of 39 early-stage MS patients with low disability, and showed higher Dice similarity coefficients (median DSC = 0.55) and higher detection rate (median DR = 61%) than two widely used methods (median DSC = 0.50, median DR < 45%) for automated MS lesion segmentation. We argue that this is due to the higher performance in segmentation of small lesions, which are inherently prone to partial volume effects.
Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation
NeuroImage. Clinical, 2017
In recent years, many automatic brain structure segmentation methods have been proposed. However, these methods are commonly tested with non-lesioned brains and the effect of lesions on their performance has not been evaluated. Here, we analyze the effect of multiple sclerosis (MS) lesions on three well-known automatic brain structure segmentation methods, namely, FreeSurfer, FIRST and multi-atlas fused by majority voting, which use learning-based, deformable and atlas-based strategies, respectively. To perform a quantitative analysis, 100 synthetic images of MS patients with a total of 2174 lesions are simulated on two public databases with available brain structure ground truth information (IBSR18 and MICCAI'12). The Dice similarity coefficient (DSC) differences and the volume differences between the healthy and the simulated images are calculated for the subcortical structures and the brainstem. We observe that the three strategies are affected when lesions are present. Howev...
Automated Segmentation of MS Lesions from Multi-channel MR Images
Lecture Notes in Computer Science, 1999
Quantitative analysis of MR images is becoming increasingly important as a surrogate marker in clinical trials in multiple sclerosis (MS). This paper describes a fully automated model-based method for segmentation of MS lesions from multi-channel MR images. The method simultaneously corrects for MR field inhomogeneities, estimates tissue class distribution parameters and classifies the image voxels. MS lesions are detected as voxels that are not well explained by the model. The results of the automated method are compared with the lesions delineated by human experts, showing a significant total lesion load correlation and an average overall spatial correspondence similar to that between the experts.
Automated segmentation of MS lesions in MR
NeuroImage, 2000
Quantitative analysis of MR images is becoming increasingly important as a surrogate marker in clinical trials in multiple sclerosis (MS). This paper describes a fully automated model-based method for segmentation of MS lesions from multi-channel MR images. The method simultaneously corrects for MR field inhomogeneities, estimates tissue class distribution parameters and classifies the image voxels. MS lesions are detected as voxels that are not well explained by the model. The results of the automated method are compared with the lesions delineated by human experts, showing a significant total lesion load correlation and an average overall spatial correspondence similar to that between the experts.