GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction

In voxel-based neuroimage analysis, lesion features have been the main focus in disease prediction due to their interpretability with respect to the related diseases. However, we observe that there exists another type of features introduced during the preprocessing steps and we call them "\textbf{Procedural Bias}". Besides, such bias can be leveraged to improve classification accuracy. Nevertheless, most existing models suffer from either under-fit without considering procedural bias or poor interpretability without differentiating such bias from lesion ones. In this paper, a novel dual-task algorithm namely \emph{GSplit LBI} is proposed to resolve this problem. By introducing an augmented variable enforced to be structural sparsity with a variable splitting term, the estimators for prediction and selecting lesion features can be optimized separately and mutually monitored by each other following an iterative scheme. Empirical experiments have been evaluated on the Alzheimer's Disease Neuroimaging Initiative\thinspace(ADNI) database. The advantage of proposed model is verified by improved stability of selected lesion features and better classification results.Comment: Conditional Accepted by Miccai,201

A Case Based Reasoning View of School Dropout Screening

The cause for student dropout is often termed as the antecedent of failure, since it stands for a key event, which leads to dropout. Indeed, school dropout is well thought out as one of the major worries of our times. It is a multi-layered and complex phenomenon, with many triggers, namely academic striving and failure, poor attendance, retention, disengagement from school or even socio-economic motives. School dropout represents economic and social losses to the individual, family and community. However, it may be prevented if the educational actors hold pro-active strategies (e.g., taking into account similar past experiences). Indeed, this work will start with the development of a decision support system to assess school dropout, centered on a formal framework based on Logic Programming for Knowledge Representation, complemented with a Case-Based Reasoning approach to problem solving, which caters for the handling of incomplete, unknown, or even contradictory information, i.e., it improves the analysis enactment of the retrieving cases process

Temporal Registration in In-Utero Volumetric MRI Time Series

We present a robust method to correct for motion and deformations in in-utero volumetric MRI time series. Spatio-temporal analysis of dynamic MRI requires robust alignment across time in the presence of substantial and unpredictable motion. We make a Markov assumption on the nature of deformations to take advantage of the temporal structure in the image data. Forward message passing in the corresponding hidden Markov model (HMM) yields an estimation algorithm that only has to account for relatively small motion between consecutive frames. We demonstrate the utility of the temporal model by showing that its use improves the accuracy of the segmentation propagation through temporal registration. Our results suggest that the proposed model captures accurately the temporal dynamics of deformations in in-utero MRI time series.National Institutes of Health (U.S.) (NIH NIBIB NAC P41EB015902)National Institutes of Health (U.S.) (NIH NICHD U01HD087211)National Institutes of Health (U.S.) (NIH NIBIB R01EB017337)Wistron CorporationMerrill Lynch Wealth Management (Fellowship

Temporal Registration in In-Utero Volumetric MRI Time Series

We present a robust method to correct for motion and deformations in in-utero volumetric MRI time series. Spatio-temporal analysis of dynamic MRI requires robust alignment across time in the presence of substantial and unpredictable motion. We make a Markov assumption on the nature of deformations to take advantage of the temporal structure in the image data. Forward message passing in the corresponding hidden Markov model (HMM) yields an estimation algorithm that only has to account for relatively small motion between consecutive frames. We demonstrate the utility of the temporal model by showing that its use improves the accuracy of the segmentation propagation through temporal registration. Our results suggest that the proposed model captures accurately the temporal dynamics of deformations in in-utero MRI time series.National Institutes of Health (U.S.) (NIH NIBIB NAC P41EB015902)National Institutes of Health (U.S.) (NIH NICHD U01HD087211)National Institutes of Health (U.S.) (NIH NIBIB R01EB017337)Wistron CorporationMerrill Lynch Wealth Management (Fellowship

CompNet: Complementary Segmentation Network for Brain MRI Extraction

Brain extraction is a fundamental step for most brain imaging studies. In this paper, we investigate the problem of skull stripping and propose complementary segmentation networks (CompNets) to accurately extract the brain from T1-weighted MRI scans, for both normal and pathological brain images. The proposed networks are designed in the framework of encoder-decoder networks and have two pathways to learn features from both the brain tissue and its complementary part located outside of the brain. The complementary pathway extracts the features in the non-brain region and leads to a robust solution to brain extraction from MRIs with pathologies, which do not exist in our training dataset. We demonstrate the effectiveness of our networks by evaluating them on the OASIS dataset, resulting in the state of the art performance under the two-fold cross-validation setting. Moreover, the robustness of our networks is verified by testing on images with introduced pathologies and by showing its invariance to unseen brain pathologies. In addition, our complementary network design is general and can be extended to address other image segmentation problems with better generalization.Comment: 8 pages, Accepted to MICCAI 201

INSIDE: Steering Spatial Attention with Non-Imaging Information in CNNs

We consider the problem of integrating non-imaging information into segmentation networks to improve performance. Conditioning layers such as FiLM provide the means to selectively amplify or suppress the contribution of different feature maps in a linear fashion. However, spatial dependency is difficult to learn within a convolutional paradigm. In this paper, we propose a mechanism to allow for spatial localisation conditioned on non-imaging information, using a feature-wise attention mechanism comprising a differentiable parametrised function (e.g. Gaussian), prior to applying the feature-wise modulation. We name our method INstance modulation with SpatIal DEpendency (INSIDE). The conditioning information might comprise any factors that relate to spatial or spatio-temporal information such as lesion location, size, and cardiac cycle phase. Our method can be trained end-to-end and does not require additional supervision. We evaluate the method on two datasets: a new CLEVR-Seg dataset where we segment objects based on location, and the ACDC dataset conditioned on cardiac phase and slice location within the volume. Code and the CLEVR-Seg dataset are available at https://github.com/jacenkow/inside.Comment: Accepted at International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 202

Ubiquitin sets the timer: impacts on aging and longevity

Protein homeostasis is essential for cellular function, organismal growth and viability. Damaged and aggregated proteins are turned over by two major proteolytic routes of the cellular quality-control pathways: the ubiquitin-proteasome system and autophagy. For both these pathways, ubiquitination provides the recognition signal for substrate selection. This Commentary discusses how ubiquitin-dependent proteolytic pathways are coordinated with stress- and aging-induced signals

Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE

Probabilistic modelling has been an essential tool in medical image analysis, especially for analyzing brain Magnetic Resonance Images (MRI). Recent deep learning techniques for estimating high-dimensional distributions, in particular Variational Autoencoders (VAEs), opened up new avenues for probabilistic modeling. Modelling of volumetric data has remained a challenge, however, because constraints on available computation and training data make it difficult effectively leverage VAEs, which are well-developed for 2D images. We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices. We do so by estimating the sample mean and covariance in the latent space of the 2D model over the slice direction. This combined model lets us sample new coherent stacks of latent variables to decode into slices of a volume. We also introduce a novel evaluation method for generated volumes that quantifies how well their segmentations match those of true brain anatomy. We demonstrate that our proposed model is competitive in generating high quality volumes at high resolutions according to both traditional metrics and our proposed evaluation.Comment: accepted for publication at MICCAI 2020. Code available https://github.com/voanna/slices-to-3d-brain-vae