GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network

We propose a novel convolutional neural network for lesion detection from weak labels. Only a single, global label per image - the lesion count - is needed for training. We train a regression network with a fully convolutional architecture combined with a global pooling layer to aggregate the 3D output into a scalar indicating the lesion count. When testing on unseen images, we first run the network to estimate the number of lesions. Then we remove the global pooling layer to compute localization maps of the size of the input image. We evaluate the proposed network on the detection of enlarged perivascular spaces in the basal ganglia in MRI. Our method achieves a sensitivity of 62% with on average 1.5 false positives per image. Compared with four other approaches based on intensity thresholding, saliency and class maps, our method has a 20% higher sensitivity.Comment: Article published in MICCAI 2017. We corrected a few errors from the first version: padding, loss, typos and update of the DOI numbe

Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects or interactions between elements. The choice of a distance or similarity metric is, however, not trivial and can be highly dependent on the application at hand. In this work, we propose a novel metric learning method to evaluate distance between graphs that leverages the power of convolutional neural networks, while exploiting concepts from spectral graph theory to allow these operations on irregular graphs. We demonstrate the potential of our method in the field of connectomics, where neuronal pathways or functional connections between brain regions are commonly modelled as graphs. In this problem, the definition of an appropriate graph similarity function is critical to unveil patterns of disruptions associated with certain brain disorders. Experimental results on the ABIDE dataset show that our method can learn a graph similarity metric tailored for a clinical application, improving the performance of a simple k-nn classifier by 11.9% compared to a traditional distance metric.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches

Finding the common structural brain connectivity network for a given population is an open problem, crucial for current neuro-science. Recent evidence suggests there's a tightly connected network shared between humans. Obtaining this network will, among many advantages , allow us to focus cognitive and clinical analyses on common connections, thus increasing their statistical power. In turn, knowledge about the common network will facilitate novel analyses to understand the structure-function relationship in the brain. In this work, we present a new algorithm for computing the core structural connectivity network of a subject sample combining graph theory and statistics. Our algorithm works in accordance with novel evidence on brain topology. We analyze the problem theoretically and prove its complexity. Using 309 subjects, we show its advantages when used as a feature selection for connectivity analysis on populations, outperforming the current approaches

Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Simultaneous Matrix Diagonalization for Structural Brain Networks Classification

This paper considers the problem of brain disease classification based on connectome data. A connectome is a network representation of a human brain. The typical connectome classification problem is very challenging because of the small sample size and high dimensionality of the data. We propose to use simultaneous approximate diagonalization of adjacency matrices in order to compute their eigenstructures in more stable way. The obtained approximate eigenvalues are further used as features for classification. The proposed approach is demonstrated to be efficient for detection of Alzheimer's disease, outperforming simple baselines and competing with state-of-the-art approaches to brain disease classification

GYG1 causing progressive limb girdle myopathy with onset during teenage years (polyglucosan body myopathy 2)

An 84-year-old lady with slowly progressive limb and axial muscle weakness with onset in her teens was referred for genetic investigations. Targeted next generation sequencing (NGS) revealed a homozygous mutation GYG1 in exon5:c.487delG:p.D163fs, confirming the diagnosis of Polyglucosan Body Myopathy 2 (PGBM2). Retrospective review of muscle pathology revealed a florid vacuolar myopathy with histochemical and ultrastructural features consistent with a polyglucosan storage myopathy. No cardiac symptoms were reported. Our case is consistent with the core phenotype of GYG1-related PGBM2 apart from an early onset of weakness without cardiac symptoms. The presence of α-amylase resistant PAS-positive material in skeletal muscle biopsy of patients with slowly progressive limb girdle muscle weakness should prompt the search for GYG1 mutations. This case highlights the combined role of muscle pathology and NGS in the molecular resolution of undiagnosed patients with undiagnosed neuromuscular conditions

Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

This paper introduces a novel methodology to integrate human brain connectomics and parcellation for brain tumor segmentation and survival prediction. For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1mm space and map this parcellation to each individual subject data. We use deep neural network architectures together with hard negative mining to achieve the final voxel level classification. For survival prediction, we present a new method for combining features from connectomics data, brain parcellation information, and the brain tumor mask. We leverage the average connectome information from the Human Connectome Project and map each subject brain volume onto this common connectome space. From this, we compute tractographic features that describe potential neural disruptions due to the brain tumor. These features are then used to predict the overall survival of the subjects. The main novelty in the proposed methods is the use of normalized brain parcellation data and tractography data from the human connectome project for analyzing MR images for segmentation and survival prediction. Experimental results are reported on the BraTS2018 dataset.Comment: 14 pages, 5 figures, 4 tables, accepted by BrainLes 2018 MICCAI worksho

A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data

We introduce a wide and deep neural network for prediction of progression from patients with mild cognitive impairment to Alzheimer's disease. Information from anatomical shape and tabular clinical data (demographics, biomarkers) are fused in a single neural network. The network is invariant to shape transformations and avoids the need to identify point correspondences between shapes. To account for right censored time-to-event data, i.e., when it is only known that a patient did not develop Alzheimer's disease up to a particular time point, we employ a loss commonly used in survival analysis. Our network is trained end-to-end to combine information from a patient's hippocampus shape and clinical biomarkers. Our experiments on data from the Alzheimer's Disease Neuroimaging Initiative demonstrate that our proposed model is able to learn a shape descriptor that augments clinical biomarkers and outperforms a deep neural network on shape alone and a linear model on common clinical biomarkers.Comment: Data and Machine Learning Advances with Multiple Views Workshop, ECML-PKDD 201

CAV3 mutations causing exercise intolerance, myalgia and rhabdomyolysis: expanding the phenotypic spectrum of caveolinopathies

Rhabdomyolysis is often due to a combination of environmental trigger(s) and genetic predisposition; however, the underlying genetic cause remains elusive in many cases. Mutations in CAV3 lead to various neuromuscular phenotypes with partial overlap, including limb girdle muscular dystrophy type 1C (LGMD1C), rippling muscle disease, distal myopathy and isolated hyperCKemia. Here we present a series of eight patients from seven families presenting with exercise intolerance and rhabdomyolysis caused by mutations in CAV3 diagnosed by next generation sequencing (NGS) (n=6). Symptoms included myalgia (n=7), exercise intolerance (n=6) and episodes of rhabdomyolysis (n=2). Percussion-induced rapid muscle contractions (PIRCs) were seen in five out of six patients examined. A previously reported heterozygous mutation in CAV3 (p.T78M) and three novel variants (p.V14I, p.F41S, p.F54V) were identified. Caveolin-3 immunolabeling in muscle was normal in 3/4 patients however, immunoblotting showed more than 50% reduction of caveolin-3 in five patients compared with controls. This case series demonstrates that exercise intolerance, myalgia and rhabdomyolysis may be caused by CAV3 mutations and broadens the phenotypic spectrum of caveolinopathies. In our series immunoblotting was a more sensitive method to detect reduced caveolin-3 levels than immunohistochemistry in skeletal muscle. Patients presenting with muscle pain, exercise intolerance and rhabdomyolysis should be routinely tested for PIRCs as this may be an important clinical clue for caveolinopathies, even in the absence of other “typical” features. The use of NGS may expand current knowledge concerning inherited diseases, and unexpected/atypical phenotypes may be attributed to well-known human disease genes