Learning under Distributed Weak Supervision

The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research

Plasma Membrane Calcium ATPase Regulates Stoichiometry of CD4+ T-Cell Compartments

Immune responses involve mobilization of T cells within naïve and memory compartments. Tightly regulated Ca2+ levels are essential for balanced immune outcomes. How Ca2+ contributes to regulating compartment stoichiometry is unknown. Here, we show that plasma membrane Ca2+ ATPase 4 (PMCA4) is differentially expressed in human CD4+ T compartments yielding distinct store operated Ca2+ entry (SOCE) profiles. Modulation of PMCA4 yielded a more prominent increase of SOCE in memory than in naïve CD4+ T cell. Interestingly, downregulation of PMCA4 reduced the effector compartment fraction and led to accumulation of cells in the naïve compartment. In silico analysis and chromatin immunoprecipitation point towards Ying Yang 1 (YY1) as a transcription factor regulating PMCA4 expression. Analyses of PMCA and YY1 expression patterns following activation and of PMCA promoter activity following downregulation of YY1 highlight repressive role of YY1 on PMCA expression. Our findings show that PMCA4 adapts Ca2+ levels to cellular requirements during effector and quiescent phases and thereby represent a potential target to intervene with the outcome of the immune 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

Reaction-diffusion model for STIM-ORAI interaction: the role of ROS and mutations

Release of $Ca^{2+}$ from endoplasmatic retriculum (ER) $Ca^{2+}$ stores causes stromal interaction molecules (STIM) in the ER membrane and ORAI proteins in the plasma membrane (PM) to interact and form the $Ca^{2+}$ release activated $Ca^{2+}$ (CRAC) channels, which represent a major $Ca^{2+}$ entry route in non-excitable cells and thus control various cell functions. It is experimentally possible to mutate ORAI1 proteins and therefore modify, especially block, the $Ca^{2+}$ influx into the cell. On the basis of the model of Hoover and Lewis (2011) [Hoover P J and Lewis R S, 2011], we formulate a reaction-diffusion model to quantify the STIM1-ORAI1 interaction during CRAC channel formation and analyze different ORAI1 channel stoichiometries and different ratios of STIM1 and ORAI1 in comparison with experimental data. We incorporate the inhibition of ORAI1 channels by ROS into our model and calculate its contribution to the CRAC channel amplitude. We observe a large decrease of the CRAC channel amplitude evoked by mutations of ORAI1 proteins

Multiple landmark detection using multi-agent reinforcement learning

The detection of anatomical landmarks is a vital step for medical image analysis and applications for diagnosis, interpretation and guidance. Manual annotation of landmarks is a tedious process that requires domain-specific expertise and introduces inter-observer variability. This paper proposes a new detection approach for multiple landmarks based on multi-agent reinforcement learning. Our hypothesis is that the position of all anatomical landmarks is interdependent and non-random within the human anatomy, thus finding one landmark can help to deduce the location of others. Using a Deep Q-Network (DQN) architecture we construct an environment and agent with implicit inter-communication such that we can accommodate K agents acting and learning simultaneously, while they attempt to detect K different landmarks. During training the agents collaborate by sharing their accumulated knowledge for a collective gain. We compare our approach with state-of-the-art architectures and achieve significantly better accuracy by reducing the detection error by 50%, while requiring fewer computational resources and time to train compared to the naïve approach of training K agents separately. Code and visualizations available: https://github.com/thanosvlo/MARL-for-Anatomical-Landmark-Detectio

Supra-Molecular Assemblies of ORAI1 at Rest Precede Local Accumulation into Puncta after Activation

The Ca2+ selective channel ORAI1 and endoplasmic reticulum (ER)-resident STIM proteins form the core of the channel complex mediating store operated Ca2+ entry (SOCE). Using liquid phase electron microscopy (LPEM), the distribution of ORAI1 proteins was examined at rest and after SOCE-activation at nanoscale resolution. The analysis of over seven hundred thousand ORAI1 positions revealed a number of ORAI1 channels had formed STIM-independent distinct supra-molecular clusters. Upon SOCE activation and in the presence of STIM proteins, a fraction of ORAI1 assembled in micron-sized two-dimensional structures, such as the known puncta at the ER plasma membrane contact zones, but also in divergent structures such as strands, and ring-like shapes. Our results thus question the hypothesis that stochastically migrating single ORAI1 channels are trapped at regions containing activated STIM, and we propose instead that supra-molecular ORAI1 clusters fulfill an amplifying function for creating dense ORAI1 accumulations upon SOCE-activation

Flexible reconstruction and correction of unpredictable motion from stacks of 2D images

We present a method to correct motion in fetal in-utero scan sequences. The proposed approach avoids previously necessary manual segmentation of a region of interest. We solve the problem of non-rigid motion by splitting motion corrupted slices into overlapping patches of finite size. In these patches the assumption of rigid motion approximately holds and they can thus be used to perform a slice-to-volume-based (SVR) reconstruction during which their consistency with the other patches is learned. The learned information is used to reject patches that are not conform with the motion corrected reconstruction in their local areas. We evaluate rectangular and evenly distributed patches for the reconstruction as well as patches that have been derived from super-pixels. Both approaches achieve on 29 subjects aged between 22–37 weeks a sufficient reconstruction quality and facilitate following 3D segmentation of fetal organs and the placenta

Supra-Molecular Assemblies of ORAI1 at Rest Precede Local Accumulation into Puncta after Activation

The Ca2+ selective channel ORAI1 and endoplasmic reticulum (ER)-resident STIM proteins form the core of the channel complex mediating store operated Ca2+ entry (SOCE). Using liquid phase electron microscopy (LPEM), the distribution of ORAI1 proteins was examined at rest and after SOCEactivation at nanoscale resolution. The analysis of over seven hundred thousand ORAI1 positions revealed a number of ORAI1 channels had formed STIM-independent distinct supra-molecular clusters. Upon SOCE activation and in the presence of STIM proteins, a fraction of ORAI1 assembled in micron-sized two-dimensional structures, such as the known puncta at the ER plasma membrane contact zones, but also in divergent structures such as strands, and ring-like shapes. Our results thus question the hypothesis that stochastically migrating single ORAI1 channels are trapped at regions containing activated STIM, and we propose instead that supra-molecular ORAI1 clusters fulfill an amplifying function for creating dense ORAI1 accumulations upon SOCE-activation

Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data

Datasets are rarely a realistic approximation of the target population. Say, prevalence is misrepresented, image quality is above clinical standards, etc. This mismatch is known as sampling bias. Sampling biases are a major hindrance for machine learning models. They cause significant gaps between model performance in the lab and in the real world. Our work is a solution to prevalence bias. Prevalence bias is the discrepancy between the prevalence of a pathology and its sampling rate in the training dataset, introduced upon collecting data or due to the practioner rebalancing the training batches. This paper lays the theoretical and computational framework for training models, and for prediction, in the presence of prevalence bias. Concretely a bias-corrected loss function, as well as bias-corrected predictive rules, are derived under the principles of Bayesian risk minimization. The loss exhibits a direct connection to the information gain. It offers a principled alternative to heuristic training losses and complements test-time procedures based on selecting an operating point from summary curves. It integrates seamlessly in the current paradigm of (deep) learning using stochastic backpropagation and naturally with Bayesian models

Evaluation of a protocol-based intervention to promote timely switching from intravenous to oral paracetamol for post-operative pain management: an interrupted time series analysis

Rationale, aims and objectives: Timely switching from intravenous to oral therapy ensures optimized treatment and efficient use of health care resources. Intravenous (IV) paracetamol is widely used for post-operative pain management but not always switched to the oral form in a timely manner, leading to unnecessary increase in expenditure. This study aims to evaluate the impact of a multifaceted intervention to promote timely switching from the IV to oral form in the post-operative setting. Methods: An evidence-based prescribing protocol was designed and implemented by the clinical pharmacy team in a single district general hospital in Egypt. The protocol specified the criteria for appropriate prescribing of IV paracetamol. Doctors were provided with information and educational sessions prior to implementation. A prospective, quasi-experimental study was undertaken to evaluate its impact on IV paracetamol utilization and costs. Data on monthly utilization and costs were recorded for 12 months before and after implementation (January 2012 to December 2013). Data were analysed using interrupted time series analysis. Results: Prior to implementation, in 2012, total spending on IV paracetamol was 674 154.00 Egyptian Pounds (L.E.) (23 668.00). There was a non-significant (P > 0.05) downward trend in utilization (−32 ampoules per month) and costs [reduction of 632 L.E. (222) per month]. Following implementation, immediate decrease in utilization and costs (P