Eloy Roura

Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations

Ground‐truth annotations from the well‐known Internet Brain Segmentation Repository (IBSR) datasets consider Sulcal cerebrospinal fluid (SCSF) voxels as gray matter. This can lead to bias when evaluating the performance of tissue segmentation methods. In this work we compare the accuracy of 10 brain tissue segmentation methods analyzing the effects of SCSF ground‐truth voxels on accuracy estimations.

A toolbox for multiple sclerosis lesion segmentation

IntroductionLesion 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 inter-rater variability. In this paper, we present a new tool for automated MS lesion segmentation using T1w and fluid-attenuated inversion recovery (FLAIR) images.MethodsOur 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.ResultsThe 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.ConclusionOur tool is implemented as a publicly available SPM8/12 extension that can be used by both the medical and research communities.

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

ABSTRACT In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch‐wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from a small (Symbol) set of labeled data of the same MRI contrast, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled Magnetic Resonance Imaging (MRI) data. We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state‐of‐the‐art MS lesion segmentation tools. Furthermore, the proposed method is also evaluated on two private MS clinical datasets, where the performance of our method is also compared with different recent public available state‐of‐the‐art MS lesion segmentation methods. At the time of writing this paper, our method is the best ranked approach on the MICCAI2008 challenge, outperforming the rest of 60 participant methods when using all the available input modalities (T1‐w, T2‐w and FLAIR), while still in the top‐rank (3rd position) when using only T1‐w and FLAIR modalities. On clinical MS data, our approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods, highly correlating (Symbol) also with the expected lesion volume. Symbol. No caption available. Symbol. No caption available. HIGHLIGHTSWe propose an automated WM lesion segmentation method for MS patient images.The approach relies on a cascade of two 7‐layer convolutional neural networks.We evaluate its accuracy with both the MICCAI2008 challenge and clinical MS data.Our approach is currently the best ranked method of the challenge (1th pos / 60).On MS data, the accuracy is significantly better that state‐of‐the‐art methods.

MARGA: Multispectral Adaptive Region Growing Algorithm for brain extraction on axial MRI

Brain extraction, also known as skull stripping, is one of the most important preprocessing steps for many automatic brain image analysis. In this paper we present a new approach called Multispectral Adaptive Region Growing Algorithm (MARGA) to perform the skull stripping process. MARGA is based on a region growing (RG) algorithm which uses the complementary information provided by conventional magnetic resonance images (MRI) such as T1-weighted and T2-weighted to perform the brain segmentation. MARGA can be seen as an extension of the skull stripping method proposed by Park and Lee (2009) [1], enabling their use in both axial views and low quality images. Following the same idea, we first obtain seed regions that are then spread using a 2D RG algorithm which behaves differently in specific zones of the brain. This adaptation allows to deal with the fact that middle MRI slices have better image contrast between the brain and non-brain regions than superior and inferior brain slices where the contrast is smaller. MARGA is validated using three different databases: 10 simulated brains from the BrainWeb database; 2 data sets from the National Alliance for Medical Image Computing (NAMIC) database, the first one consisting in 10 normal brains and 10 brains of schizophrenic patients acquired with a 3T GE scanner, and the second one consisting in 5 brains from lupus patients acquired with a 3T Siemens scanner; and 10 brains of multiple sclerosis patients acquired with a 1.5T scanner. We have qualitatively and quantitatively compared MARGA with the well-known Brain Extraction Tool (BET), Brain Surface Extractor (BSE) and Statistical Parametric Mapping (SPM) approaches. The obtained results demonstrate the validity of MARGA, outperforming the results of those standard techniques.

Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding

Magnetic resonance imaging (MRI) is frequently used to detect and segment multiple sclerosis lesions due to the detailed and rich information provided. We present a modified expectation-maximisation algorithm to segment brain tissues (white matter, grey matter, and cerebro-spinal fluid) as well as a partial volume class containing fluid and grey matter. This algorithm provides an initial segmentation in which lesions are not separated from tissue, thus a second step is needed to find them. This second step involves the thresholding of the FLAIR image, followed by a regionwise refinement to discard false detections. To evaluate the proposal, we used a database with 45 cases comprising 1.5T imaging data from three different hospitals with different scanner machines and with a variable lesion load per case. The results for our database point out to a higher accuracy when compared to two of the best state-of-the-art approaches.

Automated tissue segmentation of MR brain images in the presence of white matter lesions

&NA; Over the last few years, the increasing interest in brain tissue volume measurements on clinical settings has led to the development of a wide number of automated tissue segmentation methods. However, white matter lesions are known to reduce the performance of automated tissue segmentation methods, which requires manual annotation of the lesions and refilling them before segmentation, which is tedious and time‐consuming. Here, we propose a new, fully automated T1‐w/FLAIR tissue segmentation approach designed to deal with images in the presence of WM lesions. This approach integrates a robust partial volume tissue segmentation with WM outlier rejection and filling, combining intensity and probabilistic and morphological prior maps. We evaluate the performance of this method on the MRBrainS13 tissue segmentation challenge database, which contains images with vascular WM lesions, and also on a set of Multiple Sclerosis (MS) patient images. On both databases, we validate the performance of our method with other state‐of‐the‐art techniques. On the MRBrainS13 data, the presented approach was at the time of submission the best ranked unsupervised intensity model method of the challenge (7th position) and clearly outperformed the other unsupervised pipelines such as FAST and SPM12. On MS data, the differences in tissue segmentation between the images segmented with our method and the same images where manual expert annotations were used to refill lesions on T1‐w images before segmentation were lower or similar to the best state‐of‐the‐art pipeline incorporating automated lesion segmentation and filling. Our results show that the proposed pipeline achieved very competitive results on both vascular and MS lesions. A public version of this approach is available to download for the neuro‐imaging community. HighlightsWe propose an automated brain tissue segmentation method for MS images with lesions.The approach relies only on T1‐w to segment brain tissue but FLAIR is recommended.We evaluate its accuracy with both the MRBrainS13 challenge and MS data.Our approach was the best unsupervised ranked method of the challenge (7th position / 31).With MS data, the performance was similar to or better than the state‐of‐the‐art. Graphical abstract Figure. No caption available.

Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling

Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-w Multiple Sclerosis (MS) lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been performed. Here, we analyzed the % of error introduced by automating the lesion segmentation and filling processes in the tissue segmentation of 70 clinically isolated syndrome patient images. First of all, images were processed using the LST and SLS toolkits with different pipeline combinations that differed in either automated or manual lesion segmentation, and lesion filling or masking out lesions. Then, images processed following each of the pipelines were segmented into gray matter (GM) and white matter (WM) using SPM8, and compared with the same images where expert lesion annotations were filled before segmentation. Our results showed that fully automated lesion segmentation and filling pipelines reduced significantly the % of error in GM and WM volume on images of MS patients, and performed similarly to the images where expert lesion annotations were masked before segmentation. In all the pipelines, the amount of misclassified lesion voxels was the main cause in the observed error in GM and WM volume. However, the % of error was significantly lower when automatically estimated lesions were filled and not masked before segmentation. These results are relevant and suggest that LST and SLS toolboxes allow the performance of accurate brain tissue volume measurements without any kind of manual intervention, which can be convenient not only in terms of time and economic costs, but also to avoid the inherent intra/inter variability between manual annotations.

Advanced MRI techniques: biomarkers in neuropsychiatric lupus.

Objectives The objective of this study was to determine whether advanced MRI could provide biomarkers for diagnosis and prognosis in neuropsychiatric systemic lupus erythematosus (NPSLE). Methods Our prospective study included 28 systemic lupus erythematosus (SLE) patients with primary central NPSLE, 22 patients without NPSLE and 20 healthy controls. We used visual scales to evaluate atrophy and white matter hyperintensities, voxel-based morphometry and Freesurfer to measure brain volume, plus diffusion-tensor imaging (DTI) to assess white matter (WM) and gray matter (GM) damage. We compared the groups and correlated MRI abnormalities with clinical data. Results NPSLE patients had less GM and WM than controls (p = 0.042) in the fronto-temporal regions and corpus callosum. They also had increased diffusivities in the temporal lobe WM (p < 0.010) and reduced fractional anisotropy in the right frontal lobe WM (p = 0.018). High clinical scores, longstanding disease, and low serum C3 were associated with atrophy, lower fractional anisotropy and higher diffusivity in the fronto-temporal lobes. Antimalarial treatment correlated negatively with atrophy in the frontal cortex and thalamus; it was also associated with lower diffusivity in the fronto-temporal WM clusters. Conclusions Atrophy and microstructural damage in fronto-temporal WM and GM in NPSLE correlate with severity, activity and the time from disease onset. Antimalarial treatment seems to give some brain-protective effects.

Automated Detection of Lupus White Matter Lesions in MRI

Brain magnetic resonance imaging provides detailed information which can be used to detect and segment white matter lesions (WML). In this work we propose an approach to automatically segment WML in Lupus patients by using T1w and fluid-attenuated inversion recovery (FLAIR) images. Lupus WML appear as small focal abnormal tissue observed as hyperintensities in the FLAIR images. The quantification of these WML is a key factor for the stratification of lupus patients and therefore both lesion detection and segmentation play an important role. In our approach, the T1w image is first used to classify the three main tissues of the brain, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF), while the FLAIR image is then used to detect focal WML as outliers of its GM intensity distribution. A set of post-processing steps based on lesion size, tissue neighborhood, and location are used to refine the lesion candidates. The proposal is evaluated on 20 patients, presenting qualitative, and quantitative results in terms of precision and sensitivity of lesion detection [True Positive Rate (62%) and Positive Prediction Value (80%), respectively] as well as segmentation accuracy [Dice Similarity Coefficient (72%)]. Obtained results illustrate the validity of the approach to automatically detect and segment lupus lesions. Besides, our approach is publicly available as a SPM8/12 toolbox extension with a simple parameter configuration.

Agents for Social Search in Long-Term Digital Preservation

This paper describes the application of agents to automate information exchange for digital preservation. Agents are able to recommend preservation solutions and also apply them to different preservation situations. Trust models for question-routing and answer ranking that are implemented by means of agents, show greater performance than traditional keyword search methodologies.