vol2Brain: A New Online Pipeline for Whole Brain MRI Analysis.

Automatic and reliable quantitative tools for MR brain image analysis are a very valuable resource for both clinical and research environments. In the past few years, this field has experienced many advances with successful techniques based on label fusion and more recently deep learning. However, few of them have been specifically designed to provide a dense anatomical labeling at the multiscale level and to deal with brain anatomical alterations such as white matter lesions (WML). In this work, we present a fully automatic pipeline (vol2Brain) for whole brain segmentation and analysis, which densely labels (N > 100) the brain while being robust to the presence of WML. This new pipeline is an evolution of our previous volBrain pipeline that extends significantly the number of regions that can be analyzed. Our proposed method is based on a fast and multiscale multi-atlas label fusion technology with systematic error correction able to provide accurate volumetric information in a few minutes. We have deployed our new pipeline within our platform volBrain (www.volbrain.upv.es), which has been already demonstrated to be an efficient and effective way to share our technology with the users worldwide.

A novel deep learning based hippocampus subfield segmentation method.

The automatic assessment of hippocampus volume is an important tool in the study of several neurodegenerative diseases such as Alzheimer's disease. Specifically, the measurement of hippocampus subfields properties is of great interest since it can show earlier pathological changes in the brain. However, segmentation of these subfields is very difficult due to their complex structure and for the need of high-resolution magnetic resonance images manually labeled. In this work, we present a novel pipeline for automatic hippocampus subfield segmentation based on a deeply supervised convolutional neural network. Results of the proposed method are shown for two available hippocampus subfield delineation protocols. The method has been compared to other state-of-the-art methods showing improved results in terms of accuracy and execution time.

Deep learning based MRI contrast synthesis using full volume prediction using full volume prediction.

In Magnetic Resonance Imaging (MRI), depending on the image acquisition settings, a large number of image types or contrasts can be generated showing complementary information of the same imaged subject. This multi-spectral information is highly beneficial since can improve MRI analysis tasks such as segmentation and registration, thanks to pattern ambiguity reduction. However, the acquisition of several contrasts is not always possible due to time limitations and patient comfort constraints. Contrast synthesis has emerged recently as an approximate solution to generate other image types different from those acquired originally. Most of the previously proposed methods for contrast synthesis are slice-based which result in intensity inconsistencies between neighbor slices when applied in 3D. We propose the use of a 3D convolutional neural network (CNN) capable of generating T2 and FLAIR images from a single anatomical T1 source volume. The proposed network is a 3D variant of the UNet that processes the whole volume at once breaking with the inconsistency in the resulting output volumes related to 2D slice or patch-based methods. Since working with a full volume at once has a huge memory demand we have introduced a spatial-to-depth and a reconstruction layer that allows working with the full volume but maintain the required network complexity to solve the problem. Our approach enhances the coherence in the synthesized volume while improving the accuracy thanks to the integrated three-dimensional context-awareness. Finally, the proposed method has been validated with a segmentation method, thus demonstrating its usefulness in a direct and relevant application.

Toward a unified analysis of cerebellum maturation and aging across the entire lifespan: A MRI analysis.

Previous literature about the structural characterization of the human cerebellum is related to the context of a specific pathology or focused in a restricted age range. In fact, studies about the cerebellum maturation across the lifespan are scarce and most of them considered the cerebellum as a whole without investigating each lobule. This lack of study can be explained by the lack of both accurate segmentation methods and data availability. Fortunately, during the last years, several cerebellum segmentation methods have been developed and many databases comprising subjects of different ages have been made publically available. This fact opens an opportunity window to obtain a more extensive analysis of the cerebellum maturation and aging. In this study, we have used a recent state-of-the-art cerebellum segmentation method called CERES and a large data set (N = 2,831 images) from healthy controls covering the entire lifespan to provide a model for 12 cerebellum structures (i.e., lobules I-II, III, IV, VI, Crus I, Crus II, VIIB, VIIIA, VIIIB, IX, and X). We found that lobules have generally an evolution that follows a trajectory composed by a fast growth and a slow degeneration having sometimes a plateau for absolute volumes, and a decreasing tendency (faster in early ages) for normalized volumes. Special consideration is dedicated to Crus II, where slow degeneration appears to stabilize in elder ages for absolute volumes, and to lobule X, which does not present any fast growth during childhood in absolute volumes and shows a slow growth for normalized volumes.

pBrain: A novel pipeline for Parkinson related brain structure segmentation.

Parkinson is a very prevalent neurodegenerative disease impacting the life of millions of people worldwide. Although its cause remains unknown, its functional and structural analysis is fundamental to advance in the search of a cure or symptomatic treatment. The automatic segmentation of deep brain structures related to Parkinson`s disease could be beneficial for the follow up and treatment planning. Unfortunately, there is not broadly available segmentation software to automatically measure Parkinson related structures. In this paper, we present a novel pipeline to segment three deep brain structures related to Parkinson's disease (substantia nigra, subthalamic nucleus and red nucleus). The proposed method is based on the multi-atlas label fusion technology that works on standard and high-resolution T2-weighted images. The proposed method also includes as post-processing a new neural network-based error correction step to minimize systematic segmentation errors. The proposed method has been compared to other state-of-the-art methods showing competitive results in terms of accuracy and execution time.

Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images.

The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method.

Regional hippocampal vulnerability in early multiple sclerosis: Dynamic pathological spreading from dentate gyrus to CA1.

BACKGROUND: Whether hippocampal subfields are differentially vulnerable at the earliest stages of multiple sclerosis (MS) and how this impacts memory performance is a current topic of debate. METHOD: We prospectively included 56 persons with clinically isolated syndrome (CIS) suggestive of MS in a 1-year longitudinal study, together with 55 matched healthy controls at baseline. Participants were tested for memory performance and scanned with 3 T MRI to assess the volume of 5 distinct hippocampal subfields using automatic segmentation techniques. RESULTS: At baseline, CA4/dentate gyrus was the only hippocampal subfield with a volume significantly smaller than controls (p < .01). After one year, CA4/dentate gyrus atrophy worsened (-6.4%, p < .0001) and significant CA1 atrophy appeared (both in the stratum-pyramidale and the stratum radiatum-lacunosum-moleculare, -5.6%, p < .001 and -6.2%, p < .01, respectively). CA4/dentate gyrus volume at baseline predicted CA1 volume one year after CIS (R2 = 0.44 to 0.47, p < .001, with age, T2 lesion-load, and global brain atrophy as covariates). The volume of CA4/dentate gyrus at baseline was associated with MS diagnosis during follow-up, independently of T2-lesion load and demographic variables (p < .05). Whereas CA4/dentate gyrus volume was not correlated with memory scores at baseline, CA1 atrophy was an independent correlate of episodic verbal memory performance one year after CIS (ß = 0.87, p < .05). CONCLUSION: The hippocampal degenerative process spread from dentate gyrus to CA1 at the earliest stage of MS. This dynamic vulnerability is associated with MS diagnosis after CIS and will ultimately impact hippocampal-dependent memory performance.

HIPS: A new hippocampus subfield segmentation method.

The importance of the hippocampus in the study of several neurodegenerative diseases such as Alzheimer's disease makes it a structure of great interest in neuroimaging. However, few segmentation methods have been proposed to measure its subfields due to its complex structure and the lack of high resolution magnetic resonance (MR) data. In this work, we present a new pipeline for automatic hippocampus subfield segmentation using two available hippocampus subfield delineation protocols that can work with both high and standard resolution data. The proposed method is based on multi-atlas label fusion technology that benefits from a novel multi-contrast patch match search process (using high resolution T1-weighted and T2-weighted images). The proposed method also includes as post-processing a new neural network-based error correction step to minimize systematic segmentation errors. The method has been evaluated on both high and standard resolution images and compared to other state-of-the-art methods showing better results in terms of accuracy and execution time.

The accuracy of postoperative, non-invasive Air-Test to diagnose atelectasis in healthy patients after surgery: a prospective, diagnostic pilot study.

OBJECTIVE: To assess the diagnostic accuracy of peripheral capillary oxygen saturation (SpO2) while breathing room air for 5 min (the 'Air-Test') in detecting postoperative atelectasis. DESIGN: Prospective cohort study. Diagnostic accuracy was assessed by measuring the agreement between the index test and the reference standard CT scan images. SETTING: Postanaesthetic care unit in a tertiary hospital in Spain. PARTICIPANTS: Three hundred and fifty patients from 12 January to 7 February 2015; 170 patients scheduled for surgery under general anaesthesia who were admitted into the postsurgical unit were included. INTERVENTION: The Air-Test was performed in conscious extubated patients after a 30 min stabilisation period during which they received supplemental oxygen therapy via a venturi mask. The Air-Test was defined as positive when SpO2 was ≤96% and negative when SpO2 was ≥97%. Arterial blood gases were measured in all patients at the end of the Air-Test. In the subsequent 25 min, the presence of atelectasis was evaluated by performing a CT scan in 59 randomly selected patients. MAIN OUTCOME MEASURES: The primary study outcome was assessment of the accuracy of the Air-Test for detecting postoperative atelectasis compared with the reference standard. The secondary outcome was the incidence of positive Air-Test results. RESULTS: The Air-Test diagnosed postoperative atelectasis with an area under the receiver operating characteristic curve of 0.90 (95% CI 0.82 to 0.98) with a sensitivity of 82.6% and a specificity of 87.8%. The presence of atelectasis was confirmed by CT scans in all patients (30/30) with positive and in 5 patients (17%) with negative Air-Test results. Based on the Air-Test, postoperative atelectasis was present in 36% of the patients (62 out of 170). CONCLUSION: The Air-Test may represent an accurate, simple, inexpensive and non-invasive method for diagnosing postoperative atelectasis. TRIAL REGISTRATION: NCT02650037.

Performance of the New Turbine Mid-Level Critical Care Ventilators.

BACKGROUND: During recent years, ventilators using turbines as flow-generating systems have become increasingly more relevant. This bench study was designed to compare triggering and pressurization of 7 turbine mid-level ICU ventilators. METHODS: We used a dual-chamber lung model to test 7 mid-level ICU ventilators in pressure support mode with levels of 10, 15, and 20 cm H2O with 2 PEEP levels of 5 cm H2O and the minimum level allowed by the ventilator. A ventilator was connected to the master chamber to simulate 2 different effort levels. Pressure drop, trigger delay time, time to minimum pressure, and pressure time products (PTP) during trigger and the first 300 and 500 ms were analyzed. RESULTS: In the trigger evaluation, the Savina had the highest delay time, whereas the C2, the V60, and the Trilogy had the lowest pressure drops and PTP values in both effort levels. In pressurization capacity assessment using ideal PTP300 and PTP500 percentages, the C2 and the V680 had the best results, and the Carina and the Savina had lower values, with no differences between both effort levels. Differences between PEEP levels did not seem to be relevant. CONCLUSIONS: Pressure support mode for tested ventilators worked properly, but pressurization capacity and trigger function performance were clearly superior in the newest machines. The use of PEEP did not modify the results.

CERES: A new cerebellum lobule segmentation method.

The human cerebellum is involved in language, motor tasks and cognitive processes such as attention or emotional processing. Therefore, an automatic and accurate segmentation method is highly desirable to measure and understand the cerebellum role in normal and pathological brain development. In this work, we propose a patch-based multi-atlas segmentation tool called CERES (CEREbellum Segmentation) that is able to automatically parcellate the cerebellum lobules. The proposed method works with standard resolution magnetic resonance T1-weighted images and uses the Optimized PatchMatch algorithm to speed up the patch matching process. The proposed method was compared with related recent state-of-the-art methods showing competitive results in both accuracy (average DICE of 0.7729) and execution time (around 5 minutes).

NABS: non-local automatic brain hemisphere segmentation.

In this paper, we propose an automatic method to segment the five main brain sub-regions (i.e. left/right hemispheres, left/right cerebellum and brainstem) from magnetic resonance images. The proposed method uses a library of pre-labeled brain images in a stereotactic space in combination with a non-local label fusion scheme for segmentation. The main novelty of the proposed method is the use of a multi-label block-wise label fusion strategy specifically designed to deal with the classification of main brain sub-volumes that process only specific parts of the brain images significantly reducing the computational burden. The proposed method has been quantitatively evaluated against manual segmentations. The evaluation showed that the proposed method was faster while producing more accurate segmentations than a current state-of-the-art method. We also present evidences suggesting that the proposed method was more robust against brain pathologies than the compared method. Finally, we demonstrate the clinical value of our method compared to the state-of-the-art approach in terms of the asymmetry quantification in Alzheimer's disease.

Risk factors for Chagas disease among pregnant women in El Salvador.

OBJECTIVES: To determine the seroprevalence of Chagas disease among pregnant women and estimate the risk factors for Chagas disease during pregnancies. METHODS: Community-based serological tests on Trypanosoma cruzi and structured interviews on socio-demographic and socio-economic status were conducted with pregnant women registered at three health centres in Sonsonate province, El Salvador. RESULTS: Of 797 pregnant women participating in the study, 29 (3.6%) were infected with Chagas disease. None had clinical symptoms. The results of bivariate analyses showed the significant association between seropositivity and maternal age ≥35 years, anaemia, illiteracy, having no formal school education and having knowledge on Chagas disease (P < 0.05). The results of multivariate analysis indicate that age ≥35 years and anaemia were significantly associated with being infected with Chagas disease among pregnant women (OR = 3.541 and 5.197, respectively). CONCLUSION: We recommend that the national Chagas disease control programme be better coordinated with the national maternal and child health programme to introduce blood screening for T. cruzi during antenatal visits. If financial constraint allows systematic blood screening to be only partially implemented, resources should be focused on pregnant women ≥35 years and women who have anaemia.

Nonlocal intracranial cavity extraction.

Automatic and accurate methods to estimate normalized regional brain volumes from MRI data are valuable tools which may help to obtain an objective diagnosis and followup of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume (ICV) is often used for normalization. However, the high variability of brain shape and size due to normal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV estimation problem challenging. In this paper, we present a new approach to perform ICV extraction based on the use of a library of prelabeled brain images to capture the large variability of brain shapes. To this end, an improved nonlocal label fusion scheme based on BEaST technique is proposed to increase the accuracy of the ICV estimation. The proposed method is compared with recent state-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden.

Evaluación de la resistencia a insecticidas de una cepa de Aedes aegypti de El Salvador / Assessing the insecticide resistance of an Aedes aegypti strain in El Salvador

OBJETIVOS: Evaluar el nivel de susceptibilidad a insecticidas de una cepa de Aedes aegypti procedente de El Salvador y describir los posibles mecanismos de resistencia al temefós. MÉTODOS: Se evaluó una cepa de A. aegypti procedente del municipio de Soyapango, departamento de San Salvador, El Salvador. Mediante bioensayos se determinó la susceptibilidad de las larvas al insecticida organofosforado temefós y a tres piretroides (deltametrina, lambdacialotrina y cipermetrina) y de los adultos a un insecticida organofosforado (clorpirifós). Se determinó el factor de resistencia (FR50) con respecto a una cepa sensible de referencia (Rockefeller). Se estableció el mecanismo de resistencia al temefós mediante el empleo de sustancias sinergistas, ensayos bioquímicos de actividad enzimática y zimogramas en gel de poliacrilamida. RESULTADOS: Las larvas de la cepa estudiada mostraron una alta resistencia al temefós (FR50 = 24,16). De las enzimas analizadas, se encontró que solo la esterasa A4 estaba vinculada al mecanismo de resistencia al temefós. Los mosquitos adultos resultaron susceptibles a la lambdacialotrina y al clorpirifós y su resistencia a la deltametrina y la cipermetrina quedó en la categoría de verificación. CONCLUSIONES: La resistencia al temefós podría reducir la eficacia del control químico del mosquito A. aegypti en la zona estudiada de El Salvador. Los insecticidas clorpirifós, lambdacialotrina y cipermetrina son buenos candidatos alternativos a utilizar en las nuevas intervenciones de control de este vector.

OBJECTIVES: To assess the level of insecticide susceptibility of a certain Aedes aegypti strain found in El Salvador and to explain the mechanisms for its resistance to temephos. METHODS: An A. aegypti strain from the municipality of Soyapango, Department of San Salvador, El Salvador, was studied. Bioassays were used to determine the susceptibility of the larvae to the organophosphate insecticide temephos and to three pyrethroids (deltamethrin, lambda-cyhalothrin, and cypermethrin); and of adults to an organophosphate insecticide (chlorpyrifos). The resistance factor (RF50) with determined with respect to a reference susceptible strain (Rockefeller). The mechanism of temephos resistance was determined through the use of synergistic substances, biochemical assays for enzymatic activity, and polyacrylamide gel zymograms. RESULTS: The larvae of the strain studied proved highly resistant to temephos (RF50 = 24.16). Of the enzyme samples analyzed, only the esterase A4 was linked to the mechanism of temephos resistance. The adult mosquitoes were susceptible to lambda-cyhalothrin and chlorpyrifos; and resistance to deltamethrin and cypermethrin fell into the category needing further verification. CONCLUSIONS: Temephos resistance could reduce the efficiency of chemical control of the A. aegypti mosquito in El Salvador study area. Chlorpyrifos, lambda-cyhalothrin, and cypermethrin are good alternative insecticides for use in new efforts to control this vector.

[Assessing the insecticide resistance of an Aedes aegypti strain in El Salvador]. / Evaluación de la resistencia a insecticidas de una cepa de Aedes aegypti de El Salvador.

OBJECTIVES: To assess the level of insecticide susceptibility of a certain Aedes aegypti strain found in El Salvador and to explain the mechanisms for its resistance to temephos. METHODS: An A. aegypti strain from the municipality of Soyapango, Department of San Salvador, El Salvador, was studied. Bioassays were used to determine the susceptibility of the larvae to the organophosphate insecticide temephos and to three pyrethroids (deltamethrin, lambda-cyhalothrin, and cypermethrin); and of adults to an organophosphate insecticide (chlorpyrifos). The resistance factor (RF50) with determined with respect to a reference susceptible strain (Rockefeller). The mechanism of temephos resistance was determined through the use of synergistic substances, biochemical assays for enzymatic activity, and polyacrylamide gel zymograms. RESULTS: The larvae of the strain studied proved highly resistant to temephos (RF50 = 24.16). Of the enzyme samples analyzed, only the esterase A4 was linked to the mechanism of temephos resistance. The adult mosquitoes were susceptible to lambda-cyhalothrin and chlorpyrifos; and resistance to deltamethrin and cypermethrin fell into the category needing further verification. CONCLUSIONS: Temephos resistance could reduce the efficiency of chemical control of the A. aegypti mosquito in El Salvador study area. Chlorpyrifos, lambda-cyhalothrin, and cypermethrin are good alternative insecticides for use in new efforts to control this vector.

Evaluación de la resistencia a insecticidas de una cepa de Aedes aegypti de El Salvador / Assessing the insecticide resistance of an Aedes aegypti strain in El Salvador

Evaluar el nivel de susceptibilidad a insecticidas de una cepa de Aedes aegypti procedente de El Salvador y describir los posibles mecanismos de resistencia al temefós...(AU)