Synth-by-Reg (SbR): Contrastive Learning for Synthesis-Based Registration of Paired Images

Nonlinear inter-modality registration is often challenging due to the lack of objective functions that are good proxies for alignment. Here we propose a synthesis-by-registration method to convert this problem into an easier intra-modality task. We introduce a registration loss for weakly supervised image translation between domains that does not require perfectly aligned training data. This loss capitalises on a registration U-Net with frozen weights, to drive a synthesis CNN towards the desired translation. We complement this loss with a structure preserving constraint based on contrastive learning, which prevents blurring and content shifts due to overfitting. We apply this method to the registration of histological sections to MRI slices, a key step in 3D histology reconstruction. Results on two public datasets show improvements over registration based on mutual information (13% reduction in landmark error) and synthesis-based algorithms such as CycleGAN (11% reduction), and are comparable to registration with label supervision. Code and data are publicly available at https://github.com/acasamitjana/SynthByReg

Projection to Latent Spaces Disentangles Pathological Effects on Brain Morphology in the Asymptomatic Phase of Alzheimer's Disease

Alzheimer’s disease (AD) continuum is defined as a cascade of several neuropathological processes that can be measured using biomarkers, such as cerebrospinal fluid (CSF) levels of Aβ, p-tau, and t-tau. In parallel, brain anatomy can be characterized through imaging techniques, such as magnetic resonance imaging (MRI). In this work we relate both sets of measurements and seek associations between biomarkers and the brain structure that can be indicative of AD progression. The goal is to uncover underlying multivariate effects of AD pathology on regional brain morphological information. For this purpose, we used the projection to latent structures (PLS) method. Using PLS, we found a low dimensional latent space that best describes the covariance between both sets of measurements on the same subjects. Possible confounder effects (age and sex) on brain morphology are included in the model and regressed out using an orthogonal PLS model. We looked for statistically significant correlations between brain morphology and CSF biomarkers that explain part of the volumetric variance at each region-of-interest (ROI). Furthermore, we used a clustering technique to discover a small set of CSF-related patterns describing the AD continuum. We applied this technique to the study of subjects in the whole AD continuum, from the pre-clinical asymptomatic stages all the way through to the symptomatic groups. Subsequent analyses involved splitting the course of the disease into diagnostic categories: cognitively unimpaired subjects (CU), mild cognitively impaired subjects (MCI), and subjects with dementia (AD-dementia), where all symptoms were due to AD

Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas

Joint registration of a stack of 2D histological sections to recover 3D structure ("3D histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (ℓ2 norm, which can be minimised in closed form) and Laplacian (ℓ1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest

What Africa can do to accelerate and sustain progress against malaria

After a longstanding global presence, malaria is now largely non-existent or suppressed in most parts of the world. Today, cases and deaths are primarily concentrated in sub-Saharan Africa. According to many experts, this persistence on the African continent reflects factors such as resistance to insecticides and drugs as well as insufficient access to essential commodities such as insecticide-treated nets and effective drugs. Crucially, however, this narrative ignores many central weaknesses in the fight against malaria and instead reinforces a narrow, commodity-driven vision of disease control. This paper therefore describes the core challenges hindering malaria programs in Africa and highlights key opportunities to rethink current strategies for sustainable control and elimination. The epidemiology of malaria in Africa presents far greater challenges than elsewhere and requires context-specific initiatives tailored to national and sub-national targets. To sustain progress, African countries must systematically address key weaknesses in its health systems, improve the quality and use of data for surveillance-responses, improve both technical and leadership competencies for malaria control, and gradually reduce overreliance on commodities while expanding multisectoral initiatives such as improved housing and environmental sanitation. They must also leverage increased funding from both domestic and international sources, and support pivotal research and development efforts locally. Effective vaccines and drugs, or other potentially transformative technologies such as genedrive modified mosquitoes, could further accelerate malaria control by complementing current tools. However, our underlying strategies remain insufficient and must be expanded to include more holistic and context-specific approaches critical to achieve and sustain effective malaria control

Rethinking human resources and capacity building needs for malaria control and elimination in Africa

Despite considerable success in controlling malaria worldwide, progress toward achieving malaria elimination has largely stalled. In particular, strategies to overcome roadblocks in malaria control and elimination in Africa are critical to achieving worldwide malaria elimination goals-this continent carries 94% of the global malaria case burden. To identify key areas for targeted efforts, we combined a comprehensive review of current literature with direct feedback gathered from frontline malaria workers, leaders, and scholars from Africa. Our analysis identified deficiencies in human resources, training, and capacity building at all levels, from research and development to community involvement. Addressing these needs will require active and coordinated engagement of stakeholders as well as implementation of effective strategies, with malaria-endemic countries owning the relevant processes. This paper reports those valuable identified needs and their concomitant opportunities to accelerate progress toward the goals of the World Health Organization's Global Technical Strategy for Malaria 2016-2030. Ultimately, we underscore the critical need to re-think current approaches and expand concerted efforts toward increasing relevant human resources for health and capacity building at all levels if we are to develop the relevant competencies necessary to maintain current gains while accelerating momentum toward malaria control and elimination

NeAT: a Nonlinear Analysis Toolbox for Neuroimaging

NeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a wide range of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting and complexity for model inference and a graphical user interface (GUI) for visualization of results. We illustrate its usefulness on two study cases where non-linear effects have been previously established. Firstly, we study the nonlinear effects of Alzheimer’s disease on brain morphology (volume and cortical thickness). Secondly, we analyze the effect of the apolipoprotein APOE-ε4 genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed at https://imatge-upc.github.io/neat-tool/

DeepReg: a deep learning toolkit for medical image registration

Image fusion is a fundamental task in medical image analysis and computer-assisted intervention. Medical image registration, computational algorithms that align different images together (Hill et al., 2001), has in recent years turned the research attention towards deep learning. Indeed, the representation ability to learn from population data with deep neural networks has opened new possibilities for improving registration generalisability by mitigating difficulties in designing hand-engineered image features and similarity measures for many realworld clinical applications (Fu et al., 2020; Haskins et al., 2020). In addition, its fast inference can substantially accelerate registration execution for time-critical tasks. DeepReg is a Python package using TensorFlow (Abadi et al., 2015) that implements multiple registration algorithms and a set of predefined dataset loaders, supporting both labelledand unlabelled data. DeepReg also provides command-line tool options that enable basic and advanced functionalities for model training, prediction and image warping. These implementations, together with their documentation, tutorials and demos, aim to simplify workflows for prototyping and developing novel methodology, utilising latest development and accessing quality research advances. DeepReg is unit tested and a set of customised contributor guidelines are provided to facilitate community contributions. A submission to the MICCAI Educational Challenge has utilised the DeepReg code and demos to explore the link between classical algorithms and deep-learning-based methods (Montana Brown et al., 2020), while a recently published research work investigated temporal changes in prostate cancer imaging, by using a longitudinal registration adapted from the DeepReg code (Yang et al., 2020)

ENVEJECIMIENTO Y SOLEDAD

Objective: The aim of this study is to analyze whether elderly people in a state of fragility, visited by the Emergency Domiciliary Care service, have received a follow-up program by the nursing staff and whether they were labeled as “Elderly people in state of fragility.” Material and method: A two-phase observational study. Firstly, in phase I, a population comprising people older than 64 and visited by the emergency domiciliary service from the Raval Nord Primary Care Centre was selected. Whether they had received nursing follow-up and whether they had been diagnosed as elderly people in a frail state was analyzed. In phase II, a simple random sample from this population was chosen. Here, it was analyzed whether there had been changes in the nursing diagnosis and follow-up. Results: Of a total was seen that of 776 medical emergency domiciliary visits, 568 (73.19%) were people belonging to the over 64 group. Out of the total, 57 cases (10%) belonged to the age group between 75 and 85 years old (80%). Out of this group, 77.26% were women; 94% were diagnosed with a chronic condition (diabetes, hypertension, chronic obstructive pulmonary disease, etc); and 77.2% were polymedicated. In addition, 66.7% were not included in the Domiciliary Care Program and were not diagnosed as frail elderly people. In phase II, an increase in nursing care, as well as in the “frail elderly people” diagnosis were found. Conclusions: The use of nursing diagnosis for elderly people in a frail state is an indispensable tool for the monitoring and follow-up of that population.Objetivo: Analizar si en las personas mayores en situación de fragilidad que han sido atendidas por el servicio de urgencias domiciliarias, constaba el seguimiento por el personal de enfermería del centro y  si constaba  el diagnóstico de “Personas mayores en situación de fragilidad”. Material y método: Se realizó un estudio observacional en dos fases: 1ª se analizaron todas las personas mayores de 64 años que fueron atendidas en el servicio de urgencias domiciliarias del centro de Atención Primaría de Raval Nord, se analizó si en dicha población constaba el seguimiento de enfermería y el diagnóstico de personas mayores en situación de fragilidad ; 2ª fase, se realizó un muestreo simple  de la población analizada y se evaluó si se produjeron cambios en  los diagnósticos de enfermería y seguimiento de dicha población. Resultados: De un total de 776 visitas domiciliarias de urgencias médicas, se observó que 568 (73,19%) eran mayores de 64 años. Del total, se estudiaron 57 (10%) casos y se observó que el 80% pertenecían al grupo de edad comprendido entre 75 y 85 años; el 77,26% eran mujeres; el 94% con diagnóstico crónico (Diabetes, Hipertensión Epoc...); el 77,2% eran personas polimedicadas y el 66,7% del total no estaban incluidas en el programa de Atención Domiciliaria y no constaba ningún diagnóstico de “personas mayores frágiles”. En la segunda fase se observa un incremento  en la atención por parte de los profesionales de enfermería y un aumento en el diagnóstico de “Personas mayores frágiles”. Conclusiones: La utilización del diagnóstico de enfermería en las personas en situación de fragilidad es una herramienta imprescindible para el seguimiento y control de dicha población.  

Surfactant Protein D, a Marker of Lung Innate Immunity, Is Positively Associated With Insulin Sensitivity

Impaired lung function and innate immunity have both attracted growing interest as a potentially novel risk factor for glucose intolerance, insulin resistance, and type 2 diabetes. We aimed to evaluate whether surfactant protein D (SP-D), a lung-derived innate immune protein, was behind these associations