PadChest: A large chest x-ray image dataset with multi-label annotated reports

We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of these reports, 27% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray database suitable for training supervised models concerning radiographs, and the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/padchest/

Técnicas de conectividad cerebral y transferencia de información aplicado al estudio de la esquizofrenia

Santiago Ramón y Cajal demostró que el sistema nervioso y el cerebro estaban formados por células, al igual que el resto de los tejidos vivos. Esas células fueron llamadas neuronas y las conexiones entre ellas, la sinapsis, son fundamentales para su funcionamiento y comunicación. Todo lo que ocurre dentro del cerebro puede describirse como un entramado de corrientes eléctricas y reacciones bioquímicas entre neuronas. A partir del siglo XIX, la observación y estudio de síndromes o enfermedades debidas a lesiones cerebrales jugó un papel trascendental en el desarrollo de las neurociencias. Por primera vez fue posible establecer algunas correlaciones entre determinadas áreas del cerebro y determinadas funciones mentales superiores como el lenguaje o la memoria. Sin embargo, hace tiempo que se ha superado ese modelo localizacionista. Hoy se asume que las funciones cognitivas no están localizadas en un área cerebral específica, sino que se basan en el funcionamiento de complejos sistemas funcionales. Gracias a las técnicas de neuroimagen funcional, podemos relacionar una tarea concreta con un determinado patrón de activación cerebral, es decir, un conjunto de áreas coactivadas. Uno de los grandes retos de la neurociencia en la actualidad es consolidar el conocimiento de los patrones de actividad cerebral. Por otro lado, muchas patologías tanto neurológicas como psiquiátricas, no obedecen a una lesión focal o a la alteración de una sola área cerebral. Diferentes trastornos, como la esquizofrenia o el autismo, se entienden en la actualidad como desordenes complejos de la conectividad neural. El estudio de la conectividad neural "in vivo" es uno de los principales objetivos de las técnicas de neuroimagen. Un objetivo en un futuro próximo es que estas técnicas, que se centran en el estudio de la conectividad entre distintas áreas cerebrales, permitan mejorar de forma directa los diagnósticos y de forma indirecta los tratamientos de diversas patologías neurológicas y mentalesDe La Iglesia Vayá, MDLD. (2011). Técnicas de conectividad cerebral y transferencia de información aplicado al estudio de la esquizofrenia [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10987Palanci

Automatic Semantic Segmentation of the Lumbar Spine: Clinical Applicability in a Multi-parametric and Multi-centre Study on Magnetic Resonance Images

One of the major difficulties in medical image segmentation is the high variability of these images, which is caused by their origin (multi-centre), the acquisition protocols (multi-parametric), as well as the variability of human anatomy, the severity of the illness, the effect of age and gender, among others. The problem addressed in this work is the automatic semantic segmentation of lumbar spine Magnetic Resonance images using convolutional neural networks. The purpose is to assign a class label to each pixel of an image. Classes were defined by radiologists and correspond to different structural elements like vertebrae, intervertebral discs, nerves, blood vessels, and other tissues. The proposed network topologies are variants of the U-Net architecture. Several complementary blocks were used to define the variants: Three types of convolutional blocks, spatial attention models, deep supervision and multilevel feature extractor. This document describes the topologies and analyses the results of the neural network designs that obtained the most accurate segmentations. Several of the proposed designs outperform the standard U-Net used as baseline, especially when used in ensembles where the output of multiple neural networks is combined according to different strategies.Comment: 19 pages, 9 Figures, 8 Tables; Supplementary Material: 6 pages, 8 Table

A cloud infrastructure for scalable computing on population imaging databanks

This article describes the software architecture designed to cope with the computing demand of research usage of complex data from the imaging biobank of the Regional Ministry of Health in the Valencia Region (CS). It proposes the use of self-configured virtual clusters on top of on-premise and public cloud infrastructures. It uses a model based on recipes and autoconfiguration to deploy virtual elastic clusters that adjust themselves to the actual workload of the study, therefore reducing operating costs and preventing the need of up-front investments both at the level of the imaging biobank or the final user. All the software used is released under open-source licenses.Blanquer Espert, I.; Caballer Fernández, M.; Martí-Bonmatí, L.; Alberich Bayarri, A.; De La Iglesia Vayá, MDLD.; Martínez, J. (2015). A cloud infrastructure for scalable computing on population imaging databanks. International Journal of Image Mining. 1(2/3):175-187. doi:10.1504/IJIM.2015.073015S17518712/

Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia

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. © 2022, The Author(s)

The past, present, and future of the brain imaging data structure (BIDS)

The 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

The past, present, and future of the Brain Imaging Data Structure (BIDS)

The 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

Vascular differences between IDH-wildtype glioblastoma and astrocytoma IDH-mutant grade 4 at imaging and transcriptomic levels

[EN] Global agreement in central nervous system (CNS) tumor classification is essential for predicting patient prognosis and determining the correct course of treatment, as well as for stratifying patients for clinical trials at international level. The last update by the World Health Organization of CNS tumor classification and grading in 2021 considered, for the first time, IDH-wildtype glioblastoma and astrocytoma IDH-mutant grade 4 as different tumors. Mutations in the genes isocitrate dehydrogenase (IDH) 1 and 2 occur early and, importantly, contribute to gliomagenesis. IDH mutation produces a metabolic reprogramming of tumor cells, thus affecting the processes of hypoxia and vascularity, resulting in a clear advantage for those patients who present with IDH-mutated astrocytomas. Despite the clinical relevance of IDH mutation, current protocols do not include full sequencing for every patient. Alternative biomarkers could be useful and complementary to obtain a more reliable classification. In this sense, magnetic resonance imaging (MRI)-perfusion biomarkers, such as relative cerebral blood volume and flow, could be useful from the moment of presurgery, without incurring additional financial costs or requiring extra effort. The main purpose of this work is to analyze the vascular and hemodynamic differences between IDH-wildtype glioblastoma and IDH-mutant astrocytoma. To achieve this, we evaluate and validate the association between dynamic susceptibility contrast-MRI perfusion biomarkers and IDH mutation status. In addition, to gain a deeper understanding of the vascular differences in astrocytomas depending on the IDH mutation, we analyze the transcriptomic bases of the vascular differences.This study was funded by: Grant PID2019-104978RB-I00/AEI/10.13039/501100011033 (ALBATROSS) funded by MCIN/AEI/ 10.13039/501100011033, Grant PID2021-127110OA-I00 (PROGRESS) funded by MCIN/AEI/10.13039/501100011033 (both from Agencia de Investigacion de Espana) and by ERDF A way of making Europe.Álvarez-Torres, MDM.; López-Cerdán, A.; Andreu, Z.; Vayá, MDLI.; Fuster García, E.; García-García, F.; Garcia-Gomez, JM. (2023). Vascular differences between IDH-wildtype glioblastoma and astrocytoma IDH-mutant grade 4 at imaging and transcriptomic levels. NMR in Biomedicine. 36(11). https://doi.org/10.1002/nbm.5004361

Técnicas de análisis de posproceso en resonancia magnetica parael estudio de la conectividad cerebral

Brain connectivity is a key concept for understanding brain function. Current methods to detect and quantify different types of connectivity with neuroimaging techniques are fundamental for understanding the pathophysiology of many neurologic and psychiatric disorders. This article aims to present a critical review of the magnetic resonance imaging techniques used to measure brain connectivity within the context of the Human Connectome Project. We review techniques used to measure: a) structural connectivity b) functional connectivity (main component analysis, independent component analysis, seed voxel, meta-analysis), and c) effective connectivity (psychophysiological interactions, causal dynamic models, multivariate autoregressive models, and structural equation models). These three approaches make it possible to combine and use different statistical techniques to elaborate mathematical models in the attempt to understand the functioning of the brain. The findings obtained with these techniques must be validated by other techniques for analyzing structural and functional connectivity. This information is integrated in the Human Connectome Project where all these approaches converge to provide a representation of all the different models of connectivity. © 2011 SERAM. Publicado por Elsevier España, S.L. Todos los derechos reservados.La noción de conectividad cerebral es un aspecto clave para entender el funcionamiento cerebral. Las metodologías para detectar y cuantificar los diferentes tipos de conectividad con técnicas de neuroimagen son en la actualidad un área de estudio fundamental en la comprensión de la fisiopatología de muchos trastornos, tanto neurológicos como psiquiátricos. Con este artículo se pretende realizar una revisión crítica de las técnicas con resonancia magnética para medir la conectividad cerebral dentro del actual contexto del proyecto Conectoma. Las técnicas revisadas se dividen en: a) conectividad estructural b) conectividad funcional (análisis de componentes principales, análisis de componentes independientes, vóxel semilla, meta-análisis) y c) conectividad efectiva (interacciones psicofisiológicas, modelo dinámico causal, modelos autorregresivos multivariantes y modelo estructural de ecuaciones). Estos tres enfoques permiten combinar y utilizar distintas técnicas matemático-estadísticas cuyos resultados proporcionan modelos para intentar predecir la funcionalidad cerebral. Es necesario validar los hallazgos de estas técnicas con otras formas de análisis de la conectividad estructural y funcional. Esta información se integra dentro del proyecto Conectoma donde este conjunto de técnicas convergen para ofrecer una representación de todos los modelos de conectividad.De La Iglesia-Vayá, M.; Molina Mateo, J.; Escarti Fabra, MJ.; Martí-Bonmatí, L.; Robles Viejo, M.; Meneu, T.; Aguilar, E.... (2011). Técnicas de análisis de posproceso en resonancia magnetica parael estudio de la conectividad cerebral. Radiología. 53(3):236-245. doi:10.1016/j.rx.2010.11.007S23624553