A cross-center smoothness prior for variational Bayesian brain tissue segmentation

Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused by center-specific acquisition protocols. However, certain aspects of segmentations, such as spatial smoothness, remain relatively consistent and can be learned separately. Here we present a smoothness prior that is fit to segmentations produced at another medical center. This informative prior is presented to an unsupervised Bayesian model. The model clusters the voxel intensities, such that it produces segmentations that are similarly smooth to those of the other medical center. In addition, the unsupervised Bayesian model is extended to a semi-supervised variant, which needs no visual interpretation of clusters into tissues.Comment: 12 pages, 2 figures, 1 table. Accepted to the International Conference on Information Processing in Medical Imaging (2019

Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

Significant advances have been made towards building accu- rate automatic segmentation systems for a variety of biomedical applica- tions using machine learning. However, the performance of these systems often degrades when they are applied on new data that differ from the training data, for example, due to variations in imaging protocols. Man- ually annotating new data for each test domain is not a feasible solution. In this work we investigate unsupervised domain adaptation using ad- versarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain. Specifically, we learn domain-invariant features by learning to counter an adversarial network, which attempts to classify the domain of the input data by observing the activations of the segmentation network. Furthermore, we propose a multi-connected domain discriminator for improved adversarial training. Our system is evaluated using two MR databases of subjects with traumatic brain in- juries, acquired using different scanners and imaging protocols. Using our unsupervised approach, we obtain segmentation accuracies which are close to the upper bound of supervised domain adaptation

Transfer learning by feature-space transformation: A method for Hippocampus segmentation across scanners

Many successful approaches in MR brain segmentation use supervised voxel classification, which requires manually labeled training images that are representative of the test images to segment. However, the performance of such methods often deteriorates if training and test images are acquired with different scanners or scanning parameters, since this leads to differences in feature representations between training and test data. In this paper we propose a feature-space transformation (FST) to overcome such differences in feature representations. The proposed FST is derived from unlabeled images of a subject that was scanned with both the source and the target scan protocol. After an affine registration, these images give a mapping between source and target voxels in the feature space. This mapping is then used to map all training samples to the feature representation of the test samples. We evaluated the benefit of the proposed FST on hippocampus segmentation. Experiments were performed on two datasets: one with relatively small differences between training and test images and one with large differences. In both cases, the FST significantly improved the performance compared to using only image normalization. Additionally, we showed that our FST can be used to improve the performance of a state-of-the-art patch-based-atlas-fusion technique in case of large differences between scanners

Nonlinear Markov Random Fields Learned via Backpropagation

Although convolutional neural networks (CNNs) currently dominate competitions on image segmentation, for neuroimaging analysis tasks, more classical generative approaches based on mixture models are still used in practice to parcellate brains. To bridge the gap between the two, in this paper we propose a marriage between a probabilistic generative model, which has been shown to be robust to variability among magnetic resonance (MR) images acquired via different imaging protocols, and a CNN. The link is in the prior distribution over the unknown tissue classes, which are classically modelled using a Markov random field. In this work we model the interactions among neighbouring pixels by a type of recurrent CNN, which can encode more complex spatial interactions. We validate our proposed model on publicly available MR data, from different centres, and show that it generalises across imaging protocols. This result demonstrates a successful and principled inclusion of a CNN in a generative model, which in turn could be adapted by any probabilistic generative approach for image segmentation.Comment: Accepted for the international conference on Information Processing in Medical Imaging (IPMI) 2019, camera ready versio

Paroxetine reduces crying in young women watching emotional movies

Rationale: Crying is a unique human emotional reaction that has not received much attention from researchers. Little is known about its underlying neurobiological mechanisms, although there is some indirect evidence suggesting the involvement of central serotonin. Objectives: We examined the acute effects of the administration of 20 mg paroxetine on the crying of young, healthy females in response to emotional movies. Methods: We applied a double-blind, crossover randomised design with 25 healthy young females as study participants. On separate days, they received either paroxetine or placebo and were exposed to one of two emotional movies: 'Once Were Warriors' and 'Brian's Song'. Crying was assessed by self-report. In addition, the reactions to emotional International Affective Picture System (IAPS) pictures and mood were measured. Results: Paroxetine had a significant inhibitory effect on crying. During both films, the paroxetine group cried significantly less than the placebo group. In contrast, no effects on mood and only minor effects on the reaction to the IAPS pictures were observed. Conclusions: A single dose of paroxetine inhibits emotional crying significantly. It is not sure what the underlying mechanism is. However, since there was no effect on mood and only minor effects on the response to emotional pictures, we postulate that paroxetine mainly acts on the physiological processes involved in the crying response

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi) automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.This study was financially supported by IMDI Grant 104002002 (Brainbox) from ZonMw, the Netherlands Organisation for Health Research and Development, within kind sponsoring by Philips, the University Medical Center Utrecht, and Eindhoven University of Technology. The authors would like to acknowledge the following members of the Utrecht Vascular Cognitive Impairment Study Group who were not included as coauthors of this paper but were involved in the recruitment of study participants and MRI acquisition at the UMC Utrecht (in alphabetical order by department): E. van den Berg, M. Brundel, S. Heringa, and L. J. Kappelle of the Department of Neurology, P. R. Luijten and W. P. Th. M. Mali of the Department of Radiology, and A. Algra and G. E. H. M. Rutten of the Julius Center for Health Sciences and Primary Care. The research of Geert Jan Biessels and the VCI group was financially supported by VIDI Grant 91711384 from ZonMw and by Grant 2010T073 of the Netherlands Heart Foundation. The research of Jeroen de Bresser is financially supported by a research talent fellowship of the University Medical Center Utrecht (Netherlands). The research of Annegreet van Opbroek and Marleen de Bruijne is financially supported by a research grant from NWO (the Netherlands Organisation for Scientific Research). The authors would like to acknowledge MeVis Medical Solutions AG (Bremen, Germany) for providing MeVisLab. Duygu Sarikaya and Liang Zhao acknowledge their Advisor Professor Jason Corso for his guidance. Duygu Sarikaya is supported by NIH 1 R21CA160825-01 and Liang Zhao is partially supported by the China Scholarship Council (CSC).info:eu-repo/semantics/publishedVersio

A Cellular Pathway Involved in Clara Cell to Alveolar Type II Cell Differentiation after Severe Lung Injury

Regeneration of alveolar epithelia following severe pulmonary damage is critical for lung function. We and others have previously shown that Scgb1a1-expressing cells, most likely Clara cells, can give rise to newly generated alveolar type 2 cells (AT2s) in response to severe lung damage induced by either influenza virus infection or bleomycin treatment. In this study, we have investigated cellular pathway underlying the Clara cell to AT2 differentiation. We show that the initial intermediates are bronchiolar epithelial cells that exhibit Clara cell morphology and express Clara cell marker, Scgb1a1, as well as the AT2 cell marker, pro-surfactant protein C (pro-SPC). These cells, referred to as pro-SPC[superscript +] bronchiolar epithelial cells (or SBECs), gradually lose Scgb1a1 expression and give rise to pro-SPC[superscript +] cells in the ring structures in the damaged parenchyma, which appear to differentiate into AT2s via a process sharing some features with that observed during alveolar epithelial development in the embryonic lung. These findings suggest that SBECs are intermediates of Clara cell to AT2 differentiation during the repair of alveolar epithelia following severe pulmonary injury.Singapore-MIT Alliance for Research and Technology Center. Infectious Disease Research Grou

Aortic stiffness and brain integrity in elderly patients with cognitive and functional complaints

Lisanne Tap,1 Annegreet van Opbroek,2 Wiro J Niessen,2,3 Marion Smits,4 Francesco US Mattace-Raso1 1Department of Internal Medicine, Section of Geriatric Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; 2Department of Medical Informatics and Radiology, Biomedical Imaging Group Rotterdam, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands; 3Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands; 4Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands Purpose: Cerebral white matter lesions (WML) and brain atrophy are frequent in older persons and are associated with adverse outcomes. It has been suggested that aortic stiffness plays a role in the pathogenesis of WML and gray matter (GM) loss. There is, however, little evidence on the association between aortic stiffness and brain integrity in older patients. In this study, we investigated whether aortic stiffness is associated with WML and GM volume in older patients with cognitive and functional complaints. Patients and methods: Fazekas score was used to analyze WML on brain imaging data of 84 persons; in a subanalysis on 42 MRI scans, the exact volume of white matter hyperintensities (WMH) and GM was determined using a brain-tissue and WMH tool. Aortic stiffness, measured as aortic pulse wave velocity (aPWV) and central pulse pressure (cPP), and blood pressure levels were non-invasively measured by the Mobil-O-Graph. Results: Mean age was 76.6 (±6.8) years. Age was correlated with cPP (Spearman’s ρ =0.296, P=0.008), aPWV (r²=0.785, P<0.001) and WMH volume (r²=0.297, P<0.001). cPP did not differ between categories of Fazekas, whereas aPWV increased with increasing Fazekas score (P for trend <0.001). After additional adjustment for age, levels of aPWV did not differ between categories. Both cPP and aPWV were associated with WMH volumes (lnB 0.025, P=0.055 and lnB 0.405, P<0.001, respectively); after additional adjustment for age, estimates were less consistent. Both cPP and aPWV were negatively associated with GM volumes in multivariate analysis (B=2.805, P=0.094 and B=111.052, P=0.032). Conclusion: Higher aortic stiffness was partly associated with increased volume of WMH and decreased volume of GM and slightly influenced by blood pressure. Age also plays a role in this association in older patients. Keywords: vascular aging, white matter hyperintensities, gray matter, older persons, cognitive complaints, functional declin