Evaluation of semi-automatic image analysis tools for cerebrospinal fluid electrophoresis of IgG oligoclonal bands.

BACKGROUND: IgG concentrations in cerebrospinal fluid generally range from 20 to 45 mg/L. In multiple sclerosis immune reactions lead to intrathecal synthesis of specific IgGs that can be detected in biological fluid samples both quantitatively and qualitatively by isoelectric focusing of supplementary oligoclonal IgG bands. METHOD: A simple tool, using the MATLAB application, to facilitate and improve isoelectric focusing profile analysis is presented and evaluated in terms of its sensitivity, repeatability and reproducibility. A comparison between human readers and semi-automatic method has also been performed. RESULTS: Results from the semi-automatic method were found to be equivalent or superior to generally employed laboratory methods. Repeatability analysis for semi-automatic processing yielded coefficients of variation (CVs) in the 3-7% range, and using a sample with an estimated IgG concentration of 200 mg/L, four bands were still visible after dilution to 5 mg/L, corresponding to band concentrations of 1.1-1.6 mg/L. Discordances between visual inspection and automatic analysis only appear at threshold levels for interpretation (the gray zone). CONCLUSION: The semi-automatic method has acceptable performance for routine implementation.

Semi-automated image analysis of gel electrophoresis of cerebrospinal fluid for oligoclonal band detection.

Detection of oligoclonal electrophoretic bands in cerebrospinal fluid (CSF) is an important diagnostic tool for Multiple Sclerosis (MS). Electrophoretic profiles are difficult to interpret due to low contrast and artefacts. A semi-automated method to ease analysis and to reduce subjectivity is presented. The method sequentially converts color images to grayscale, realigns bands, removes artifacts, then converts 2D images to a signal, before detecting, thresholding and editing peaks to optimize profiles. Such treated profiles (21 positive and 15 negative) are compared to ground truth analysis of an expert biologist. 16 profiles over 21 are well detected positive and 12 profiles over 15 are detected negative, results seem similar to inter-experts variability reported in literature.

Three-dimensional skeletonization and symbolic description in vascular imaging: preliminary results.

OBJECTIVE: A general method was developed to analyze and describe tree-like structures needed for evaluation of complex morphology, such as the cerebral vascular tree. Clinical application of the method in neurosurgery includes planning of the surgeon's intraoperative gestures. METHOD: We have developed a 3D skeletonization method adapted to tubular forms with symbolic description. This approach implements an iterative Dijkstra minimum cost spanning tree, allowing a branch-by-branch skeleton extraction. The proposed method was implemented using the laboratory software platform (ArtiMed). The 3D skeleton approach was tested on simulated data and preliminary trials on clinical datasets mainly based on magnetic resonance image acquisitions. RESULTS: A specific experimental evaluation plan was designed to test the skeletonization and symbolic description methods. Accuracy was tested by calculating the positioning error, and robustness was verified by comparing the results on a series of 18 rotations of the initial volume. Accuracy evaluation showed a Haussdorff's distance always smaller than 17 voxels and Dice's similarity coefficient greater than 70 %. CONCLUSION: Our method of symbolic description enables the analysis and interpretation of a vascular network obtained from angiographic images. The method provides a simplified representation of the network in the form of a skeleton, as well as a description of the corresponding information in a tree-like view.

Improvements of Adaptive Filtering by Optimal Projection to filter different artifact types on long duration EEG recordings.

Adaptive Filtering by Optimal Projection (AFOP) is an automatic method for reducing ocular and muscular artifacts on electro-encephalographic (EEG) recordings. This paper presents two additions to this method: an improvement of the stability of ocular artifact filtering and an adaptation of the method for filtering electrode artifacts. With these improvements, it is possible to reduce almost all the current types of artifacts, while preserving brain signals, particularly those characterising epilepsy. This generalised method consists of dividing the signal into several time-frequency windows, and in applying different spatial filters to each. Two steps are required to define one of these spatial filters: the first step consists of defining artifact spatial projection using the Common Spatial Pattern (CSP) method and the second consists of defining EEG spatial projection via regression. For this second step, a progressive orthogonalisation process is proposed to improve stability. This method has been tested on long-duration EEG recordings of epileptic patients. A neurologist quantified the ratio of removed artifacts and the ratio of preserved EEG. Among the 330 artifacted pages used for evaluation, readability was judged better for 78% of pages, equal for 20% of pages, and worse for 2%. Artifact amplitudes were reduced by 80% on average. At the same time, brain sources were preserved in amplitude from 70% to 95% depending on the type of waves (alpha, theta, delta, spikes, etc.). A blind comparison with manual Independent Component Analysis (ICA) was also realised. The results show that this method is competitive and useful for routine clinical practice.

[Automatic image segmentation for treatment planning in radiotherapy]. / Segmentation automatique des images pour la planification dosimétrique en radiothérapie.

One drawback of the growth in conformal radiotherapy and image-guided radiotherapy is the increased time needed to define the volumes of interest. This also results in inter- and intra-observer variability. However, developments in computing and image processing have enabled these tasks to be partially or totally automated. This article will provide a detailed description of the main principles of image segmentation in radiotherapy, its applications and the most recent results in a clinical context.

Dijkstra's algorithm applied to 3D skeletonization of the brain vascular tree: evaluation and application to symbolic.

This paper describes the methodology and the evaluation of a 3D skeletonization algorithm applied on brain vascular structure. This method is based on the application of the minimum cost-spanning tree using Dijkstra's algorithm and seems well appropriate to tubular objects. We briefly describe the different steps, from the segmentation to the skeleton analysis. Besides, we propose an original evaluation scheme of the method based on digital phantom and clinical data. The final aim of this work is to provide a symbolic description framework applied to cerebro-vascular structures.

[Comparison of spectral analysis and non-linear analysis of EEG in patients with cognitive decline]. / Comparaison de l'analyse spectrale et de l'analyse non-linéaire de l'EEG dans les troubles cognitifs.

Twenty-five elder subjects were classified in two groups according to the MMS score and the cognitive evoked potentials. Normal subjects (n = 15) had mean MMS = 27.6 and mean P3 amplitude = 7.1 uV), while patients with cognitive decline (n = 10) had respective values of 18 (MMS) and 3.3 uV (P3). Spectral analysis and non-linear analysis of EEG (recurrence plots of dynamical systems) were performed and both showed statistically significant differences between groups for all the parameters analysed. Subjects' classification with discriminant analysis was slightly better using the non-linear parameters. The recurrence plot method applied to EEGs, gave similar results as the dimension of correlation (D2) calculation, and was in favour of a more constraint and less complex dynamics of brain activity associated with cognitive decline.