HeMIS: Hetero-Modal Image Segmentation

We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities. Instead of attempting to impute or synthesize missing data, the proposed approach learns, for each modality, an embedding of the input image into a single latent vector space for which arithmetic operations (such as taking the mean) are well defined. Points in that space, which are averaged over modalities available at inference time, can then be further processed to yield the desired segmentation. As such, any combinatorial subset of available modalities can be provided as input, without having to learn a combinatorial number of imputation models. Evaluated on two neurological MRI datasets (brain tumors and MS lesions), the approach yields state-of-the-art segmentation results when provided with all modalities; moreover, its performance degrades remarkably gracefully when modalities are removed, significantly more so than alternative mean-filling or other synthesis approaches.Comment: Accepted as an oral presentation at MICCAI 201

Spatial fuzzy c-means thresholding for semiautomated calculation of percentage lung ventilated volume from hyperpolarized gas and (1) H MRI

Purpose To develop an image-processing pipeline for semiautomated (SA) and reproducible analysis of hyperpolarized gas lung ventilation and proton anatomical magnetic resonance imaging (MRI) scan pairs. To compare results from the software for total lung volume (TLV), ventilated volume (VV), and percentage lung ventilated volume (%VV) calculation to the current manual “basic” method and a K-means segmentation method. Materials and Methods Six patients were imaged with hyperpolarized 3He and same-breath lung 1H MRI at 1.5T and six other patients were scanned with hyperpolarized 129Xe and separate-breath 1H MRI. One expert observer and two users with experience in lung image segmentation carried out the image analysis. Spearman (R), Intraclass (ICC) correlations, Bland–Altman limits of agreement (LOA), and Dice Similarity Coefficients (DSC) between output lung volumes were calculated. Results When comparing values of %VV, agreement between observers improved using the SA method (mean; R = 0.984, ICC = 0.980, LOA = 7.5%) when compared to the basic method (mean; R = 0.863, ICC = 0.873, LOA = 14.2%) nonsignificantly (pR = 0.25, pICC = 0.25, and pLOA = 0.50 respectively). DSC of VV and TLV masks significantly improved (P < 0.01) using the SA method (mean; DSCVV = 0.973, DSCTLV = 0.980) when compared to the basic method (mean; DSCVV = 0.947, DSCTLV = 0.957). K-means systematically overestimated %VV when compared to both basic (mean overestimation = 5.0%) and SA methods (mean overestimation = 9.7%), and had poor agreement with the other methods (mean ICC; K-means vs. basic = 0.685, K-means vs. SA = 0.740). Conclusion A semiautomated image processing software was developed that improves interobserver agreement and correlation of lung ventilation volume percentage when compared to the currently used basic method and provides more consistent segmentations than the K-means method. Level of Evidence: 3 Technical Efficacy: Stage

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

Retinal blood vessels extraction using probabilistic modelling

© 2014 Kaba et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The analysis of retinal blood vessels plays an important role in detecting and treating retinal diseases. In this review, we present an automated method to segment blood vessels of fundus retinal image. The proposed method could be used to support a non-intrusive diagnosis in modern ophthalmology for early detection of retinal diseases, treatment evaluation or clinical study. This study combines the bias correction and an adaptive histogram equalisation to enhance the appearance of the blood vessels. Then the blood vessels are extracted using probabilistic modelling that is optimised by the expectation maximisation algorithm. The method is evaluated on fundus retinal images of STARE and DRIVE datasets. The experimental results are compared with some recently published methods of retinal blood vessels segmentation. The experimental results show that our method achieved the best overall performance and it is comparable to the performance of human experts.The Department of Information Systems, Computing and Mathematics, Brunel University

Mindfulness Meditation as an Adjunct to Esketamine Treatment for Major Depressive Disorder

Background: Major Depressive Disorder (MDD) affects approximately 300 million people worldwide. Despite available treatments, many patients do not achieve full recovery with traditional antidepressants, and others lack access to evidence-based care. Esketamine, a glutamatergic agent approved for treatment-resistant depression, and mindfulness meditation represent promising approaches for depression care that may improve neuroplasticity. The neuroplasticity theory of depression posits that reduced neural plasticity contributes to MDD development, with both esketamine and mindfulness potentially addressing this deficit. Objectives: This quality improvement project aimed to implement and evaluate the feasibility and efficacy of adding mindfulness meditation to esketamine treatments for patients with MDD, with the goal of improving depression outcomes. Methods: A quasi-experimental pilot was conducted at an outpatient psychiatric clinic with 19 participants (10 in mindfulness plus esketamine group, 9 in usual care esketamine-only group). Outcome measures included changes in depressive symptoms using the Patient Health Questionnaire-8 (PHQ-8), mindfulness domains of self-compassion and acting mindfully using Five Facet Mindfulness Questionnaire-Short Form (FFMQ-SF) subscales, and Hope, Agency, and Opportunity (HAO) scores. The intervention consisted of structured mindfulness meditations during esketamine treatments. Data were analyzed using t-tests, regression modeling, and Pearson’s r correlations. Results: Patients receiving the mindfulness adjunct had significantly greater PHQ-8 score reductions (4.70 points) compared to the usual care group (1.00 point; p = .014), with a large effect size (Cohen’s d = 1.27). Within the mindfulness group, self-compassion scores significantly increased (p &lt; .01) and were strongly correlated with PHQ-8 score improvements (r = -0.71, p = .032). Both groups showed excellent treatment adherence. All mindfulness group participants reported satisfaction with the intervention. Conclusion: Adding mindfulness meditations to esketamine treatments provided clinically meaningful improvements in depressive symptoms compared to esketamine alone. The strong correlation between increased self-compassion and depression symptom reduction highlights a potential mechanism for this effect. This feasible, well-received intervention offers a promising approach to enhance outcomes for patients with MDD undergoing esketamine treatment. Future research should examine optimal timing, duration, and long-term benefits of this integrative approach

Cerebellar gray and white matter volume and their relation with age and manual motor performance in healthy older adults

ObjectivesFunctional neuroimaging and voxel‐based morphometry studies have confirmed the important role of the cerebellum in motor behavior. However, little is known about the relationship between cerebellar gray (GMv) and white matter (WMv) volume and manual motor performance in aging individuals. This study aims to quantify the relationship between cerebellar tissue volume and manual motor performance.Experimental designTo gain more insight into cerebellar function and how it relates to the role of the primary motor cortex (M1), we related cerebellar GMv, WMv, and M1v to manual motor performance in 217 healthy older individuals. Left and right cerebellar GMv and WMv, and M1v were obtained using FreeSurfer. The following motor measures were obtained: grip force, tapping speed, bimanual visuomotor coordination, and manual dexterity.Principal observationsSignificant positive relationships were observed between cerebellar GMv and WMv and grip strength, right cerebellar WMv and right‐hand tapping speed, right cerebellar WMv and dexterity, M1v and grip strength, and right M1v and left‐hand dexterity, though effect sizes were small.ConclusionsOur results show that cerebellar GMv and WMv are differently associated with manual motor performance. These associations partly overlap with the brain‐behavior associations between M1 and manual motor performance. Not all observed associations were lateralized (i.e., ipsilateral cerebellar and contralateral M1v associations with motor performance), which could point to age‐related neural dedifferentiation. The current study provides new insights in the role of the cerebellum in manual motor performance. In consideration of the small effect sizes replication studies are needed to validate these results. Hum Brain Mapp 36:2352–2363, 2015. © 2015 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111082/1/hbm22775.pd

Rationale, design and methodology of the image analysis protocol for studies of patients with cerebral small vessel disease and mild stroke

Rationale: Cerebral small vessel disease (SVD) is common in ageing and patients with dementia and stroke. Its manifestations on magnetic resonance imaging (MRI) include white matter hyperintensities, lacunes, microbleeds, perivascular spaces, small subcortical infarcts, and brain atrophy. Many studies focus only on one of these manifestations. A protocol for the differential assessment of all these features is, therefore, needed. Aims: To identify ways of quantifying imaging markers in research of patients with SVD and operationalize the recommendations from the STandards for ReportIng Vascular changes on nEuroimaging guidelines. Here, we report the rationale, design, and methodology of a brain image analysis protocol based on our experience from observational longitudinal studies of patients with nondisabling stroke. Design: The MRI analysis protocol is designed to provide quantitative and qualitative measures of disease evolution including: acute and old stroke lesions, lacunes, tissue loss due to stroke, perivascular spaces, microbleeds, macrohemorrhages, iron deposition in basal ganglia, substantia nigra and brain stem, brain atrophy, and white matter hyperintensities, with the latter separated into intense and less intense. Quantitative measures of tissue integrity such as diffusion fractional anisotropy, mean diffusivity, and the longitudinal relaxation time are assessed in regions of interest manually placed in anatomically and functionally relevant locations, and in others derived from feature extraction pipelines and tissue segmentation methods. Morphological changes that relate to cognitive deficits after stroke, analyzed through shape models of subcortical structures, complete the multiparametric image analysis protocol. Outcomes: Final outcomes include guidance for identifying ways to minimize bias and confounds in the assessment of SVD and stroke imaging biomarkers. It is intended that this information will inform the design of studies to examine the underlying pathophysiology of SVD and stroke, and to provide reliable, quantitative outcomes in trials of new therapies and preventative strategies