Large deep neural networks for MS lesion segmentation

Multiple sclerosis (MS) is a multi-factorial autoimmune disorder, characterized by spatial and temporal dissemination of brain lesions that are visible in T2-weighted and Proton Density (PD) MRI. Assessment of lesion burden and is useful for monitoring the course of the disease, and assessing correlates of clinical outcomes. Although there are established semi-automated methods to measure lesion volume, most of them require human interaction and editing, which are time consuming and limits the ability to analyze large sets of data with high accuracy. The primary objective of this work is to improve existing segmentation algorithms and accelerate the time consuming operation of identifying and validating MS lesions. In this paper, a Deep Neural Network for MS Lesion Segmentation is implemented. The MS lesion samples are extracted from the Partners Comprehensive Longitudinal Investigation of Multiple Sclerosis (CLIMB) study. A set of 900 subjects with T2, PD and a manually corrected label map images were used to train a Deep Neural Network and identify MS lesions. Initial tests using this network achieved a 90% accuracy rate. A secondary goal was to enable this data repository for big data analysis by using this algorithm to segment the remaining cases available in the CLIMB repository

Longitudinal change in autonomic symptoms predicts activities of daily living and depression in Parkinson’s disease

Purpose: The primary objective of this study was to examine the relationship of longitudinal changes in autonomic symptom burden and longitudinal changes in activities of daily living (ADLs); a secondary analysis examined the impact of depressive symptoms in this relationship. Methods: Data were retrieved from the Parkinson’s Progression Markers Initiative (PPMI), a dataset documenting the natural history of newly diagnosed Parkinson’s disease (PD). The analysis focused on data from baseline, visit 6 (24 months after enrollment), and visit 12 (60 months after enrollment). The impact of longitudinal changes in autonomic symptom burden on longitudinal changes in ADLs function was examined. A secondary mediation analysis was performed to investigate whether longitudinal changes in depressive symptoms mediate the relationship between longitudinal changes in autonomic symptom burden and ADLs function. Results: Changes in autonomic symptom burden, cognitive function, depressive symptoms, and motor function all correlated with ADLs. Only changes in ADLs and depression were found to be associated with changes in autonomic symptom burden. We found that longitudinal change in autonomic symptoms was a significant predictor of change in ADLs at 24 and 60 months after enrollment, with the cardiovascular subscore being a major driver of this association. Mediation analysis revealed that the association between autonomic symptoms and ADLs is partially mediated by depressive symptoms. Conclusions: Longitudinal changes in autonomic symptoms impact ADLs function in patients with early signs of PD, both directly and indirectly through their impact on depressive symptoms. Future investigation into the influence of treatment of these symptoms on outcomes in PD is warranted

Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis

In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients. In particular, we train and test both methods on early stage disease patients, to verify their performance in challenging conditions, more similar to a clinical setting than what is typically provided in multiple sclerosis segmentation challenges. Furthermore, we evaluate a prototype naive combination of the two methods, which refines the final segmentation. All methods were trained on 32 patients, and the evaluation was performed on a pure test set of 73 cases. Results show low lesion-wise false positives (30%) for the deep learning architecture, whereas the shallow architecture yields the best Dice coefficient (63%) and volume difference (19%). Combining both shallow and deep architectures further improves the lesion-wise metrics (69% and 26% lesion-wise true and false positive rate, respectively).Comment: Accepted to the MICCAI 2018 Brain Lesion (BrainLes) worksho

Comparison of two methods for quantitative assessment of mandibular asymmetry using cone beam computed tomography image volumes

The aim of this study was to compare two methods of measuring mandibular asymmetry. The first method uses mirroring of the mandible in the midsagittal plane; the second uses mirroring of the mandible and registration on the cranial base

SVA: Shape variation analyzer

Temporo-mandibular osteo arthritis (TMJ OA) is characterized by progressive cartilage degradation and subchondral bone remodeling. The causes of this pathology remain unclear. Current research efforts are concentrated in finding new biomarkers that will help us understand disease progression and ultimately improve the treatment of the disease. In this work, we present Shape Variation Analyzer (SVA), the goal is to develop a noninvasive technique to provide information about shape changes in TMJ OA. SVA uses neural networks to classify morphological variations of 3D models of the mandibular condyle. The shape features used for training include normal vectors, curvature and distances to average models of the condyles. The selected features are purely geometric and are shown to favor the classification task into 6 groups generated by consensus between two clinician experts. With this new approach, we were able to accurately classify 3D models of condyles. In this paper, we present the methods used and the results obtained with this new tool

A web-based system for statistical shape analysis in temporomandibular joint osteoarthritis

This study presents a web-system repository: Data Storage for Computation and Integration (DSCI) for Osteoarthritis of the temporomandibular joint (TMJ OA). This environment aims to maintain and allow contributions to the database from multi-clinical centers and compute novel statistics for disease classification. For this purpose, imaging datasets stored in the DSCI consisted of three-dimensional (3D) surface meshes of condyles from CBCT, clinical markers and biological markers in healthy and TMJ OA subjects. A clusterpost package was included in the web platform to be able to execute the jobs in remote computing grids. The DSCI application allowed runs of statistical packages, such as the Multivariate Functional Shape Data Analysis to compute global correlations between covariates and the morphological variability, as well as local p-values in the 3D condylar morphology. In conclusion, the DSCI allows interactive advanced statistical tools for non-statistical experts

Use of High Resolution 3D Diffusion Tensor Imaging to Study Brain White Matter Development in Live Neonatal Rats

High resolution diffusion tensor imaging (DTI) can provide important information on brain development, yet it is challenging in live neonatal rats due to the small size of neonatal brain and motion-sensitive nature of DTI. Imaging in live neonatal rats has clear advantages over fixed brain scans, as longitudinal and functional studies would be feasible to understand neuro-developmental abnormalities. In this study, we developed imaging strategies that can be used to obtain high resolution 3D DTI images in live neonatal rats at postnatal day 5 (PND5) and PND14, using only 3 h of imaging acquisition time. An optimized 3D DTI pulse sequence and appropriate animal setup to minimize physiological motion artifacts are the keys to successful high resolution 3D DTI imaging. Thus, a 3D rapid acquisition relaxation enhancement DTI sequence with twin navigator echoes was implemented to accelerate imaging acquisition time and minimize motion artifacts. It has been suggested that neonatal mammals possess a unique ability to tolerate mild-to-moderate hypothermia and hypoxia without long term impact. Thus, we additionally utilized this ability to minimize motion artifacts in magnetic resonance images by carefully suppressing the respiratory rate to around 15/min for PND5 and 30/min for PND14 using mild-to-moderate hypothermia. These imaging strategies have been successfully implemented to study how the effect of cocaine exposure in dams might affect brain development in their rat pups. Image quality resulting from this in vivo DTI study was comparable to ex vivo scans. fractional anisotropy values were also similar between the live and fixed brain scans. The capability of acquiring high quality in vivo DTI imaging offers a valuable opportunity to study many neurological disorders in brain development in an authentic living environment

Maternal choline supplementation in a sheep model of first trimester binge alcohol fails to protect against brain volume reductions in peripubertal lambs

Fetal alcohol spectrum disorder (FASD) is a leading potentially preventable birth defect. Poor nutrition may contribute to adverse developmental outcomes of prenatal alcohol exposure, and supplementation of essential micronutrients such as choline has shown benefit in rodent models. The sheep model of first-trimester binge alcohol exposure was used in this study to model the dose of maternal choline supplementation used in an ongoing prospective clinical trial involving pregnancies at risk for FASD. Primary outcome measures included volumetrics of the whole brain, cerebellum, and pituitary derived from magnetic resonance imaging (MRI) in 6-month-old lambs, testing the hypothesis that alcohol-exposed lambs would have brain volume reductions that would be ameliorated by maternal choline supplementation. Pregnant sheep were randomly assigned to one of five groups – heavy binge alcohol (HBA; 2.5 g/kg/treatment ethanol), heavy binge alcohol plus choline supplementation (HBC; 2.5 g/kg/treatment ethanol and 10 mg/kg/day choline), saline control (SC), saline control plus choline supplementation (SCC; 10 mg/kg/day choline), and normal control (NC). Ewes were given intravenous alcohol (HBA, HBC; mean peak BACs of ~280 mg/dL) or saline (SC, SCC) on three consecutive days per week from gestation day (GD) 4–41; choline was administered on GD 4–148. MRI scans of lamb brains were performed postnatally on day 182. Lambs from both alcohol groups (with or without choline) showed significant reductions in total brain volume; cerebellar and pituitary volumes were not significantly affected. This is the first report of MRI-derived volumetric brain reductions in a sheep model of FASD following binge-like alcohol exposure during the first trimester. These results also indicate that maternal choline supplementation comparable to doses in human studies fails to prevent brain volume reductions typically induced by first-trimester binge alcohol exposure. Future analyses will assess behavioral outcomes along with regional brain and neurohistological measures

Shape variation analyzer: A classifier for temporomandibular joint damaged by osteoarthritis

We developed a deep learning neural network, the Shape Variation Analyzer (SVA), that allows disease staging of bony changes in temporomandibular joint (TMJ) osteoarthritis (OA). The sample was composed of 259 TMJ CBCT scans for the training set and 34 for the testing dataset. The 3D meshes had been previously classified in 6 groups by 2 expert clinicians. We improved the robustness of the training data using data augmentation, SMOTE, to alleviate over-fitting and to balance classes. We combined geometrical features and a shape descriptor, heat kernel signature, to describe every shape. The results were compared to nine different supervised machine learning algorithms. The deep learning neural network was the most accurate for classification of TMJ OA. In conclusion, SVA is a 3D Slicer extension that classifies pathology of the temporomandibular joint osteoarthritis cases based on 3D morphology