20 research outputs found
Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia
Get PDFThe 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. © 2022, The Author(s)
The past, present, and future of the Brain Imaging Data Structure (BIDS)
Get PDFThe Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of
data and metadata from a growing range of neuroscience modalities. This paper is meant as a
history of how the standard has developed and grown over time. We outline the principles
behind the project, the mechanisms by which it has been extended, and some of the challenges
being addressed as it evolves. We also discuss the lessons learned through the project, with the
aim of enabling researchers in other domains to learn from the success of BIDS
Classification of Alzheimer’s disease stages from magnetic resonance images using deep learning
No full textAlzheimer’s disease (AD) is a progressive type of dementia characterized by loss of memory and other cognitive abilities, including speech. Since AD is a progressive disease, detection in the early stages is essential for the appropriate care of the patient throughout its development, going from asymptomatic to a stage known as mild cognitive impairment (MCI), and then progressing to dementia and severe dementia; is worth mentioning that everyone suffers from cognitive impairment to some degree as we age, but the relevant task here is to identify which people are most likely to develop AD. Along with cognitive tests, evaluation of the brain morphology is the primary tool for AD diagnosis, where atrophy and loss of volume of the frontotemporal lobe are common features in patients who suffer from the disease. Regarding medical imaging techniques, magnetic resonance imaging (MRI) scans are one of the methods used by specialists to assess brain morphology. Recently, with the rise of deep learning (DL) and its successful implementation in medical imaging applications, it is of growing interest in the research community to develop computer-aided diagnosis systems that can help physicians to detect this disease, especially in the early stages where macroscopic changes are not so easily identified. This article presents a DL-based approach to classifying MRI scans in the different stages of AD, using a curated set of images from Alzheimer’s Disease Neuroimaging Initiative and Open Access Series of Imaging Studies databases. Our methodology involves image pre-processing using FreeSurfer, spatial data-augmentation operations, such as rotation, flip, and random zoom during training, and state-of-the-art 3D convolutional neural networks such as EfficientNet, DenseNet, and a custom siamese network, as well as the relatively new approach of vision transformer architecture. With this approach, the best detection percentage among all four architectures was around 89% for AD vs. Control, 80% for Late MCI vs. Control, 66% for MCI vs. Control, and 67% for Early MCI vs. Control
A Novel In Vivo Model for Multiplexed Analysis of Callosal Connections upon Cortical Damage
No full textBrain damage is the major cause of permanent disability and it is particularly relevant in the elderly. While most studies focused on the immediate phase of neuronal loss upon injury, much less is known about the process of axonal regeneration after damage. The development of new refined preclinical models to investigate neuronal regeneration and the recovery of brain tissue upon injury is a major unmet challenge. Here, we present a novel experimental paradigm in mice that entails the (i) tracing of cortico-callosal connections, (ii) a mechanical lesion of the motor cortex, (iii) the stereological and histological analysis of the damaged tissue, and (iv) the functional characterization of motor deficits. By combining conventional microscopy with semi-automated 3D reconstruction, this approach allows the analysis of fine subcellular structures, such as axonal terminals, with the tridimensional overview of the connectivity and tissue integrity around the lesioned area. Since this 3D reconstruction is performed in serial sections, multiple labeling can be performed by combining diverse histological markers. We provide an example of how this methodology can be used to study cellular interactions. Namely, we show the correlation between active microglial cells and the perineuronal nets that envelop parvalbumin interneurons. In conclusion, this novel experimental paradigm will contribute to a better understanding of the molecular and cellular interactions underpinning the process of cortical regeneration upon brain damage
POSITIVE - Tables
No full textTables describing the distributions of each of the positive images. They contain information such as subject, session, file path, partition (train, dev, test), and image projection (frontal, lateral)
