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

PialNN: A fast deep learning framework for cortical pial surface reconstruction

Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI). In this work, we introduce Pial Neural Network (PialNN), a 3D deep learning framework for pial surface reconstruction. PialNN is trained end-to-end to deform an initial white matter surface to a target pial surface by a sequence of learned deformation blocks. A local convolutional operation is incorporated in each block to capture the multi-scale MRI information of each vertex and its neighborhood. This is fast and memory-efficient, which allows reconstructing a pial surface mesh with 150k vertices in one second. The performance is evaluated on the Human Connectome Project (HCP) dataset including T1-weighted MRI scans of 300 subjects. The experimental results demonstrate that PialNN reduces the geometric error of the predicted pial surface by 30% compared to state-of-the-art deep learning approaches. The codes are publicly available at https://github.com/m-qiang/PialNN

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

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a single investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patch-wise optimization, super-resolution, and automatic outlier rejection. We further describe and provide an efficient parallel implementation of PVR allowing its execution within reasonable time on commercially available graphics processing units (GPU), enabling its use in the clinical practice. We evaluate PVR's computational overhead compared to standard methods and observe improved reconstruction accuracy in presence of affine motion artifacts of approximately 30% compared to conventional SVR in synthetic experiments. Furthermore, we have evaluated our method qualitatively and quantitatively on real fetal MRI data subject to maternal breathing and sudden fetal movements. We evaluate peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and cross correlation (CC) with respect to the originally acquired data and provide a method for visual inspection of reconstruction uncertainty. With these experiments we demonstrate successful application of PVR motion compensation to the whole uterus, the human fetus, and the human placenta.Comment: 10 pages, 13 figures, submitted to IEEE Transactions on Medical Imaging. v2: wadded funders acknowledgements to preprin

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

High-Performance Motion Correction of Fetal MRI

Fetal Magnetic Resonance Imaging (MRI) shows promising results for pre-natal diagnostics. The detection of potentially lifethreatening abnormalities in the fetus can be difficult with ultrasound alone. MRI is one of the few safe alternative imaging modalities in pregnancy. However, to date it has been limited by unpredictable fetal and maternal motion during acquisition. Motion between the acquisitions of individual slices of a 3D volume results in spatial inconsistencies that can be resolved by slice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms to solve this problem have evolved from very slow implementations targeting a single organ to general high-performance solutions to reconstruct the whole uterus. In this paper we give a brief overview over the current state-of-the art in fetal motion compensation methods and show currently emerging clinical applications of these technique

Molecular Characterization of Chromosome 7 in AML and MDS Patients

Myelodysplastic syndromes (MDS) share many features with acute myeloid leukemias (AML) and in fact 20 - 40% of the patients eventually develop a picture of full blown AML. Chromosome 7 has been a focus of attention as a site harboring tumor suppressor genes whose loss of function contributes to leukemia transformation or tumor progression. Abnormalities of chromosome 7 are frequently encountered in AML and MDS. The aim of the present study was to detect the molecular abnormalities of chromosome 7 in Egyptian AML and MDS patients using the FISH technique and whether the abnormality has an implication on the prognosis of the disease after a period of one year follow up. Fluorescence in-situ hybridization (FISH) was performed for chromosome 7 using a locus specific probe for 7q31 and a centromeric probe from 7p11.1-q11.1 in a series of 30 patients diagnosed as: AML (20 patients) and MDS (10 patients) according to the FAB criteria. Aberrations of Chromosome 7 were found in 36.6% of AML patients: 3 cases showing monosomy with a mean positivity of 17.3%, 2 cases showing 7q deletion with a mean positivity of 11%. While both monosomy and deletion were detected in 3 cases. However, in MDS patients; monosomy for chromosome 7 was the only abnormality detected and was found in 30% of cases. Genetic abnormality of chromosome 7 showed a significant association with poor prognostic criteria. Patients who had normal FISH results showed a higher percentage (31.6%) of complete remission (CR) versus 0% in patients with monosomy or deletion who showed a higher percentage (100%) of death or poor response to therapy (NR). Although AML patients had a worse prognosis when compared to MDS patients, patients with genetic abnormalities showed the worst outcome