RESUMO

Focal cortical dysplasia (FCD) is a congenital brain malformation that is closely associated with epilepsy. Early and accurate diagnosis is essential for effectively treating and managing FCD. Magnetic resonance imaging (MRI)-one of the most commonly used non-invasive neuroimaging methods for evaluating the structure of the brain-is often implemented along with automatic methods to diagnose FCD. In this review, we define three categories for FCD identification based on MRI: visual, semi-automatic, and fully automatic methods. By conducting a systematic review following the PRISMA statement, we identified 65 relevant papers that have contributed to our understanding of automatic FCD identification techniques. The results of this review present a comprehensive overview of the current state-of-the-art in the field of automatic FCD identification and highlight the progress made and challenges ahead in developing reliable, efficient methods for automatic FCD diagnosis using MRI images. Future developments in this area will most likely lead to the integration of these automatic identification tools into medical image-viewing software, providing neurologists and radiologists with enhanced diagnostic capabilities. Moreover, new MRI sequences and higher-field-strength scanners will offer improved resolution and anatomical detail for precise FCD characterization. This review summarizes the current state of automatic FCD identification, thereby contributing to a deeper understanding and the advancement of FCD diagnosis and management.

Assuntos

Displasia Cortical Focal , Humanos , Imageamento por Ressonância Magnética , Neuroimagem , Encéfalo , Software

RESUMO

The emergence of COVID-19 as a global pandemic forced researchers worldwide in various disciplines to investigate and propose efficient strategies and/or technologies to prevent COVID-19 from further spreading. One of the main challenges to be overcome is the fast and efficient detection of COVID-19 using deep learning approaches and medical images such as Chest Computed Tomography (CT) and Chest X-ray images. In order to contribute to this challenge, a new dataset was collected in collaboration with "S.E.S Hospital Universitario de Caldas" ( https://hospitaldecaldas.com/ ) from Colombia and organized following the Medical Imaging Data Structure (MIDS) format. The dataset contains 7,307 chest X-ray images divided into 3,077 and 4,230 COVID-19 positive and negative images. Images were subjected to a selection and anonymization process to allow the scientific community to use them freely. Finally, different convolutional neural networks were used to perform technical validation. This dataset contributes to the scientific community by tackling significant limitations regarding data quality and availability for the detection of COVID-19.

Assuntos

COVID-19 , Humanos , COVID-19/diagnóstico por imagem , Raios X , Colômbia

RESUMO

COVID-19 global pandemic affects health care and lifestyle worldwide, and its early detection is critical to control cases' spreading and mortality. The actual leader diagnosis test is the Reverse transcription Polymerase chain reaction (RT-PCR), result times and cost of these tests are high, so other fast and accessible diagnostic tools are needed. Inspired by recent research that correlates the presence of COVID-19 to findings in Chest X-ray images, this papers' approach uses existing deep learning models (VGG19 and U-Net) to process these images and classify them as positive or negative for COVID-19. The proposed system involves a preprocessing stage with lung segmentation, removing the surroundings which does not offer relevant information for the task and may produce biased results; after this initial stage comes the classification model trained under the transfer learning scheme; and finally, results analysis and interpretation via heat maps visualization. The best models achieved a detection accuracy of COVID-19 around 97%.

RESUMO

Focal cortical dysplasias (FCDs) are a frequent cause of epilepsy. It has been reported that up to 40% of them cannot be visualized with conventional magnetic resonance imaging (MRI). The main objective of this work was to evaluate by means of a retrospective descriptive observational study whether the automated brain segmentation is useful for detecting FCD. One hundred and fifty-five patients, who underwent surgery between the years 2009 and 2016, were reviewed. Twenty patients with FCD confirmed by histology and a preoperative segmentation study, with ages ranging from 3 to 43 years (14 men), were analyzed. Three expert neuroradiologists visually analyzed conventional and advanced MRI with automated segmentation. They were classified into positive and negative concerning visualization of FCD by consensus. Of the 20 patients evaluated with conventional MRI, 12 were positive for FCD. Of the negative studies for FCD with conventional MRI, 2 (25%) were positive when they were analyzed with automated segmentation. In 13 of the 20 patients (with positive segmentation for FCD), cortical thickening was observed in 5 (38.5%), while pseudothickening was observed in the rest of patients (8, 61.5%) in the anatomical region of the brain corresponding to the dysplasia. This work demonstrated that automated brain segmentation helps to increase detection of FCDs that are unable to be visualized in conventional MRI images.

Assuntos

Encéfalo/diagnóstico por imagem , Epilepsia/diagnóstico por imagem , Imageamento por Ressonância Magnética/métodos , Malformações do Desenvolvimento Cortical/diagnóstico por imagem , Adolescente , Adulto , Encéfalo/patologia , Encéfalo/cirurgia , Criança , Pré-Escolar , Epilepsia/patologia , Epilepsia/cirurgia , Feminino , Humanos , Imageamento por Ressonância Magnética/normas , Masculino , Malformações do Desenvolvimento Cortical/patologia , Malformações do Desenvolvimento Cortical/cirurgia , Estudos Retrospectivos , Adulto Jovem

RESUMO

Electroencephalography (EEG) is the standard diagnosis method for a wide variety of diseases such as epilepsy, sleep disorders, encephalopathies, and coma, among others. Resting-state functional magnetic resonance (rs-fMRI) is currently a technique used in research in both healthy individuals as well as patients. EEG and fMRI are procedures used to obtain direct and indirect measurements of brain neural activity: EEG measures the electrical activity of the brain using electrodes placed on the scalp, and fMRI detects the changes in blood oxygenation that occur in response to neural activity. EEG has a high temporal resolution and low spatial resolution, while fMRI has high spatial resolution and low temporal resolution. Thus, the combination of EEG with rs-fMRI using different methods could be very useful for research and clinical applications. In this article, we describe and show the results of a new methodology for processing rs-fMRI using seeds positioned according to the 10-10 EEG standard. We analyze the functional connectivity and adjacency matrices obtained using 65 seeds based on 10-10 EEG scheme and 21 seeds based on 10-20 EEG. Connectivity networks are created using each 10-20 EEG seeds and are analyzed by comparisons to the seven networks that have been found in recent studies. The proposed method captures high correlation between contralateral seeds, ipsilateral and contralateral occipital seeds, and some in the frontal lobe.