Better Cardiac Image Segmentation by Highly Recurrent Neural Networks

Cardiac magnetic resonance (CMR) image segmentation has been a crucial tool for medical professionals to diagnose cardiovascular diseases (CVDs), which are the leading causes of death throughout the world. Segmenting CMR images is very time consuming and increases the cost of CVD diagnoses and treatment, making them inaccessible to many. Automated CMR image segmentation models strive to lower the cost of CVD diagnosis, but such models must be efficient and accurate in such failure-sensitive domains as human medicine. This thesis proposes to apply γ-Net, a recurrent extension of the popular U-Net, to automatically perform high-quality CMR image segmentation. γ-Net is a recent development by Linsley et al. of Brown University, and has exhibited the ability to outperform U-Net on very small datasets, which is beneficial given the very limited amount of patient CMR data available to the scientific community. γ-Net leverages biological principles backed by anatomical evidence as well as attention mechanisms in order to achieve its high efficiency.In this thesis, we examine the following topics: (a) γ-Net’s resilience to smaller training set sizes, which is cruicial when little patient data is available; (b) resilience to variation in training and validation data, which is shown to significantly degrade performance in state-of-the- art models; and (c) the ability to transfer to new datasets with minimal fine tuning, which saves training cost for practical applications. We have found that (a) γ-Net significantly outperforms an equivalent U-Net in validation performance when trained using a reduced training set; (b) γ-Net is much more resilient to input variations than U-Net; and (c) γ-Net generalizes to new datasets better than comparable U-Nets

Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has the potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on the power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.Comment: See the final version published in Frontiers in Oncology here: https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful

Molecular dissection of the retinal projectome

The retina transforms visual sensation into perception. Extracted visual features are encoded by retinal ganglion cells (RGCs), the output neurons of the eye, and sent to the brain in parallel processing channels. Morphologically, RGCs fall into more than fifty diverse types, which innervate distinct brain areas. Such visual pathways differentially regulate various behaviors. However, the genetic determinants of RGC type diversity are unknown and thus we lack genetic access to study visual pathways. A generation of a more comprehensive RGC type atlas integrating molecular, morphological and functional properties is essential to dissect the functional architecture of the visual system. In a collaborative effort, I used single cell transcriptomics to molecularly classify RGCs during larval and adult stages. RGC types segregate into many discrete transcriptional clusters each with a unique molecular composition. Relatedness of clusters revealed a molecular taxonomy, in which RGC types are arranged into major RGC groups that comprise subclasses and diversify into individual types. This organization of RGC type diversification underlies a code of gene expression patterns, composed primarily of transcription factors. Differential gene expression analysis identified dozens of novel cluster-specific genetic markers for RGC types. Comparison of transcriptional signatures revealed that larval RGCs exhibit higher molecular diversity, which facilitates segregation of similar types, while adult RGCs maintain a core molecular identity suggesting a tight correspondence between larval and adult RGC types. Next, I mapped transcriptional clusters to RGC morphotypes. Select candidate markers were exploited as genetic entry points in a CRISPR-Cas9 transgenesis approach. To restrict labeling specifically to cluster-specific RGC types, I established a genetic intersection with a broad RGC marker. This intersectional transgenic approach allowed to correspond various clusters to distinct morphologically classified RGC types. I generated two transgenic lines using RGC subclass markers, one of which is based on the transcription factor eomesa expressed by RGC types routing to visual areas in hypothalamus, pretectum and tectum. Based on homologies to RGC types characterized in other species, I hypothesized that eomesa+ RGCs constitute intrinsically photosensitive RGCs and have non-image forming functions. I tested this hypothesis by characterizing their response profiles to a battery of visual stimuli and found that they are not tuned to canonical pattern stimuli. Rather eomesa+ RGCs encode ambient luminance levels corroborating my hypothesis. I further tested their necessity in non-image forming behavior, specifically visual background adaptation, which by initial investigation appears to not be affected by chemogenetic ablation of eomesa+ RGCs. In conclusion, this thesis presents a strong foundation for a RGC type atlas and reconciles molecular, morphological and functional features of discrete cell types. This comprehensive molecular classification of RGC types, together with the identified markers and newly established transgenic tools, provides a rich resource towards a better understanding of visual pathway function

Altered Neurocircuitry in the Dopamine Transporter Knockout Mouse Brain

The plasma membrane transporters for the monoamine neurotransmitters dopamine, serotonin, and norepinephrine modulate the dynamics of these monoamine neurotransmitters. Thus, activity of these transporters has significant consequences for monoamine activity throughout the brain and for a number of neurological and psychiatric disorders. Gene knockout (KO) mice that reduce or eliminate expression of each of these monoamine transporters have provided a wealth of new information about the function of these proteins at molecular, physiological and behavioral levels. In the present work we use the unique properties of magnetic resonance imaging (MRI) to probe the effects of altered dopaminergic dynamics on meso-scale neuronal circuitry and overall brain morphology, since changes at these levels of organization might help to account for some of the extensive pharmacological and behavioral differences observed in dopamine transporter (DAT) KO mice. Despite the smaller size of these animals, voxel-wise statistical comparison of high resolution structural MR images indicated little morphological change as a consequence of DAT KO. Likewise, proton magnetic resonance spectra recorded in the striatum indicated no significant changes in detectable metabolite concentrations between DAT KO and wild-type (WT) mice. In contrast, alterations in the circuitry from the prefrontal cortex to the mesocortical limbic system, an important brain component intimately tied to function of mesolimbic/mesocortical dopamine reward pathways, were revealed by manganese-enhanced MRI (MEMRI). Analysis of co-registered MEMRI images taken over the 26 hours after introduction of Mn^(2+) into the prefrontal cortex indicated that DAT KO mice have a truncated Mn^(2+) distribution within this circuitry with little accumulation beyond the thalamus or contralateral to the injection site. By contrast, WT littermates exhibit Mn^(2+) transport into more posterior midbrain nuclei and contralateral mesolimbic structures at 26 hr post-injection. Thus, DAT KO mice appear, at this level of anatomic resolution, to have preserved cortico-striatal-thalamic connectivity but diminished robustness of reward-modulating circuitry distal to the thalamus. This is in contradistinction to the state of this circuitry in serotonin transporter KO mice where we observed more robust connectivity in more posterior brain regions using methods identical to those employed here

A novel plasticity rule can explain the development of sensorimotor intelligence

Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, the self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no system specific modifications of the DEP rule but arise rather from the underlying mechanism of spontaneous symmetry breaking due to the tight brain-body-environment coupling. The new synaptic rule is biologically plausible and it would be an interesting target for a neurobiolocal investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution.Comment: 18 pages, 5 figures, 7 video

Molecular dissection of the retinal projectome

The retina transforms visual sensation into perception. Extracted visual features are encoded by retinal ganglion cells (RGCs), the output neurons of the eye, and sent to the brain in parallel processing channels. Morphologically, RGCs fall into more than fifty diverse types, which innervate distinct brain areas. Such visual pathways differentially regulate various behaviors. However, the genetic determinants of RGC type diversity are unknown and thus we lack genetic access to study visual pathways. A generation of a more comprehensive RGC type atlas integrating molecular, morphological and functional properties is essential to dissect the functional architecture of the visual system. In a collaborative effort, I used single cell transcriptomics to molecularly classify RGCs during larval and adult stages. RGC types segregate into many discrete transcriptional clusters each with a unique molecular composition. Relatedness of clusters revealed a molecular taxonomy, in which RGC types are arranged into major RGC groups that comprise subclasses and diversify into individual types. This organization of RGC type diversification underlies a code of gene expression patterns, composed primarily of transcription factors. Differential gene expression analysis identified dozens of novel cluster-specific genetic markers for RGC types. Comparison of transcriptional signatures revealed that larval RGCs exhibit higher molecular diversity, which facilitates segregation of similar types, while adult RGCs maintain a core molecular identity suggesting a tight correspondence between larval and adult RGC types. Next, I mapped transcriptional clusters to RGC morphotypes. Select candidate markers were exploited as genetic entry points in a CRISPR-Cas9 transgenesis approach. To restrict labeling specifically to cluster-specific RGC types, I established a genetic intersection with a broad RGC marker. This intersectional transgenic approach allowed to correspond various clusters to distinct morphologically classified RGC types. I generated two transgenic lines using RGC subclass markers, one of which is based on the transcription factor eomesa expressed by RGC types routing to visual areas in hypothalamus, pretectum and tectum. Based on homologies to RGC types characterized in other species, I hypothesized that eomesa+ RGCs constitute intrinsically photosensitive RGCs and have non-image forming functions. I tested this hypothesis by characterizing their response profiles to a battery of visual stimuli and found that they are not tuned to canonical pattern stimuli. Rather eomesa+ RGCs encode ambient luminance levels corroborating my hypothesis. I further tested their necessity in non-image forming behavior, specifically visual background adaptation, which by initial investigation appears to not be affected by chemogenetic ablation of eomesa+ RGCs. In conclusion, this thesis presents a strong foundation for a RGC type atlas and reconciles molecular, morphological and functional features of discrete cell types. This comprehensive molecular classification of RGC types, together with the identified markers and newly established transgenic tools, provides a rich resource towards a better understanding of visual pathway function