White matter – MICLab

A new approach for longitudinal study of white matter lesion based on texture variation

MIC-Suporte — Wed, 06 Apr 2022 12:26:54 +0000

A new approach for longitudinal study of white matter lesion based on texture variation

XXV Congresso Brasileiro de Engenharia Biomédica, p. 963-966, 2016

Lesions within the white matter brain are often observed on neurology and psychiatric patients, on both symptomatic and asymptomatic patients. The analysis of those lesions is a non-trivial task due to its variation on shape, location and severity. In order to help specialists to accomplish the correct diagnosis and follow-up, there are several computer-assisted tools that aim to automatically detect/segment these lesions, and to quantitatively evaluate the lesion progression over time. However, none of these tools characterize these lesions, or study its variation over time. This paper proposes a different approach to perform longitudinal study of white matter lesions that evaluate the variation of lesioned tissues over time through textural statistics, such as mean intensity and uniformity. This approach makes possible not only the quantitative longitudinal evaluation of lesions size, but also the analysis of their variation and etiology over time.

Full paper here: https://drive.google.com/drive/folders/0B543adcG1FClQ21ZaFhCUmdwMlk

Reduction of cerebral and corpus callosum volumes in childhood-onset systemic lupus erythematosus

MIC-Suporte — Tue, 05 Apr 2022 12:28:06 +0000

Reduction of Cerebral and Corpus Callosum Volumes in Childhood-Onset Systemic Lupus Erythematosus

Systemic Lupus Erythematosus, Volume 68, Issue 9, September 2016. Pages 2193-2199

There have been few studies in which the prevalence of cerebral atrophy in childhood-onset systemic lupus erythematosus (SLE) was evaluated using magnetic resonance imaging (MRI) volumetric measurements. This study was undertaken to determine the prevalence of cerebral and corpus callosum atrophy in childhood-onset SLE and to determine the possible relationships between atrophy and clinical, laboratory, and treatment features of the disease.

Full paper here: https://doi.org/10.1002/art.39680

Divergence map from diffusion tensor imaging: Concepts and application to corpus callosum

MIC-Suporte — Mon, 04 Apr 2022 12:11:43 +0000

Divergence map from diffusion tensor imaging: Concepts and application to corpus callosum

Engineering in Medicine and Biology Society (EMBC), 2016

This work proposes a novel approach to the analysis of Diffusion Tensor Imaging (DTI) by applying the mathematical concept of divergence, used in vector analysis. This is achieved by choosing an arbitrary direction of analysis and using this direction to transform the diffusion tensor field into an oriented vector field. The method was inspired by the idea of imposing a liquid flow inside the biological tissues, oriented in the direction of analysis, and watching the direction it would be expected to take as it flows through the paths created by the fibers. The experiments were conducted for the particular case of the analysis of the corpus callosum, using real DTI from several subjects. Results showed that the divergence map allows extraction of useful information about the spatial organization of the corpus callosum, providing a way to determine a reference plane that could be used, for example, in studies involving intersubject comparison.

Full paper here: https://doi.org/10.1109/EMBC.2016.7590900

Etiology-based classification of brain white matter hyperintensity on magnetic resonance imaging

MIC-Suporte — Sun, 03 Apr 2022 12:18:06 +0000

Etiology-based classification of brain white matter hyperintensity on magnetic resonance imaging

Journal of Medical Imaging, 2015

Brain white matter lesions found upon magnetic resonance imaging are often observed in psychiatric or neurological patients. Individuals with these lesions present a more significant cognitive impairment when compared with individuals without them. We propose a computerized method to distinguish tissue containing white matter lesions of different etiologies (e.g., demyelinating or ischemic) using texture-based classifiers. Texture attributes were extracted from manually selected regions of interest and used to train and test supervised classifiers. Experiments were conducted to evaluate texture attribute discrimination and classifiers’ performances. The most discriminating texture attributes were obtained from the gray-level histogram and from the co-occurrence matrix. The best classifier was the support vector machine, which achieved an accuracy of 87.9% in distinguishing lesions with different etiologies and an accuracy of 99.29% in distinguishing normal white matter from white matter lesions.

Full paper here: https://doi.org/10.1117/1.JMI.2.1.014002