Learning Conditional Deformable Templates with Convolutional Networks

We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks. Conventional methods for template creation and image alignment to the template have undergone decades of rich technical development. In these frameworks, templates are constructed using an iterative process of template estimation and alignment, which is often computationally very expensive. Due in part to this shortcoming, most methods compute a single template for the entire population of images, or a few templates for specific sub-groups of the data. In this work, we present a probabilistic model and efficient learning strategy that yields either universal or conditional templates, jointly with a neural network that provides efficient alignment of the images to these templates. We demonstrate the usefulness of this method on a variety of domains, with a special focus on neuroimaging. This is particularly useful for clinical applications where a pre-existing template does not exist, or creating a new one with traditional methods can be prohibitively expensive. Our code and atlases are available online as part of the VoxelMorph library at http://voxelmorph.csail.mit.edu.Comment: NeurIPS 2019: Neural Information Processing Systems. Keywords: deformable templates, conditional atlases, diffeomorphic image registration, probabilistic models, neuroimagin

Placental Growth Factor Contributes to Micro-Vascular Abnormalization and Blood-Retinal Barrier Breakdown in Diabetic Retinopathy

OBJECTIVE: There are controversies regarding the pro-angiogenic activity of placental growth factor (PGF) in diabetic retinopathy (DR). For a better understanding of its role on the retina, we have evaluated the effect of a sustained PGF over-expression in rat ocular media, using ciliary muscle electrotransfer (ET) of a plasmid encoding rat PGF-1 (pVAX2-rPGF-1). MATERIALS AND METHODS: pVAX2-rPGF-1 ET in the ciliary muscle (200 V/cm) was achieved in non diabetic and diabetic rat eyes. Control eyes received saline or naked plasmid ET. Clinical follow up was carried out over three months using slit lamp examination and fluorescein angiography. After the control of rPGF-1 expression, PGF-induced effects on retinal vasculature and on the blood-external barrier were evaluated respectively by lectin and occludin staining on flat-mounts. Ocular structures were visualized through histological analysis. RESULTS: After fifteen days of rPGF-1 over-expression in normal eyes, tortuous and dilated capillaries were observed. At one month, microaneurysms and moderate vascular sprouts were detected in mid retinal periphery in vivo and on retinal flat-mounts. At later stages, retinal pigmented epithelial cells demonstrated morphological abnormalities and junction ruptures. In diabetic retinas, PGF expression rose between 2 and 5 months, and, one month after ET, rPGF-1 over-expression induced glial activation and proliferation. CONCLUSION: This is the first demonstration that sustained intraocular PGF production induces vascular and retinal changes similar to those observed in the early stages of diabetic retinopathy. PGF and its receptor Flt-1 may therefore be looked upon as a potential regulatory target at this stage of the disease

Learning Deformable Templates for Brain MRI

Deformable templates, or atlases, are images, often labelled, that represent a typical anatomy for a population. They are commonly used in medical image analysis for population studies and computational anatomy tasks. Practitioners use image alignment techniques to compare the subject scan and the template. Unfortunately, developing a template is a computationally expensive process with existing methods. Usually, at most one template is available per population of images or anatomy. As a results, analysis is often conducted with sub-optimal templates. In this thesis, we propose a machine learning framework that uses convolutional alignment neural networks to efficiently create both unconditional and conditional templates and the corresponding label maps. We demonstrate our method on a large 3D brain MRI dataset. This is particularly relevant in medical image analysis where templates are difficult to build. We show that this framework can learn sharp templates representative of the population. These templates are representative of the population. Moreover, they can leverage label maps when available. Our method enables rapid registration of any brain image to our template. Moreover, our method has the options of producing representative conditional templates, given subject specific attributes.S.M

Learning conditional deformable templates with convolutional networks

We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks. Conventional methods for template creation and image alignment to the template have undergone decades of rich technical development. In these frameworks, templates are constructed using an iterative process of template estimation and alignment, which is often computationally very expensive. Due in part to this shortcoming, most methods compute a single template for the entire population of images, or a few templates for specific sub-groups of the data. In this work, we present a probabilistic model and efficient learning strategy that yields either universal or conditional templates, jointly with a neural network that provides efficient alignment of the images to these templates. We demonstrate the usefulness of this method on a variety of domains, with a special focus on neuroimaging. This is particularly useful for clinical applications where a pre-existing template does not exist, or creating a new one with traditional methods can be prohibitively expensive. Our code and atlases are available online as part of the VoxelMorph library at http://voxelmorph.csail.mit.edu.National Institutes of Health (U.S.) (Grants R01LM012719, R01AG053949, and 1R21AG050122)National Science Foundation (U.S.). Career Grant (1748377)National Science Foundation (U.S.). NeuroNex Grant (1707312

Pergolide Treatment of Cognitive Deficits Associated with Schizotypal Personality Disorder: Continued Evidence of the Importance of the Dopamine System in the Schizophrenia Spectrum

Cognitive deficits observed in schizophrenia are also frequently found in individuals with other schizophrenia spectrum disorders, such as schizotypal personality disorder (SPD). Dopamine appears to be a particularly important modulator of cognitive processes such as those impaired in schizophrenia spectrum disorders. In a double-blind, placebo-controlled clinical trial, we administered pergolide, a dopamine agonist targeting D1 and D2 receptors, to 25 participants with SPD and assessed the effect of pergolide treatment, as compared with placebo, on neuropsychological performance. We found that the pergolide group showed improvements in visual-spatial working memory, executive functioning, and verbal learning and memory. These results suggest that dopamine agonists may provide benefit for the cognitive abnormalities of schizophrenia spectrum disorders