Anatomy Commons^™

Anal Fistulotomy (Seton Placement), James M. Nottingham, Rebecca M. Rentea

Manuscripts, Articles, Book Chapters and Other Papers

An anal fistula (anorectal fistula) is one of the most common colorectal issues in the U.S. An anal or anorectal fistula is an inflammatory tract or connection between the anal canal and the perianal skin. Classic anal fistulas are the result of a perineal infection and abscess formation. These infections arise from the anal glands, which form a cryptoglandular abscess at the dentate line and then communicate outward to the perianal skin. The abscess or infections can spontaneously drain externally or be drained by a surgical incision and drainage procedure. After the drainage, a chronic tract can form that will …

Bioenergetic Functions In Subpopulations Of Heart Mitochondria Are Preserved In A Non-Obese Type 2 Diabetes Rat Model (Goto-Kakizaki), Nicola Lai, C. M. Kummitha, F. Loy, R. Isola, C. L. Hoppel

Electrical & Computer Engineering Faculty Publications

A distinct bioenergetic impairment of heart mitochondrial subpopulations in diabetic cardiomyopathy is associated with obesity; however, many type 2 diabetic (T2DM) patients with high-risk for cardiovascular disease are not obese. In the absence of obesity, it is unclear whether bioenergetic function in the subpopulations of mitochondria is affected in heart with T2DM. To address this issue, a rat model of non-obese T2DM was used to study heart mitochondrial energy metabolism, measuring bioenergetics and enzyme activities of the electron transport chain (ETC). Oxidative phosphorylation in the presence of substrates for ETC and ETC activities in both populations of heart mitochondria in …

Context Aware Deep Learning For Brain Tumor Segmentation, Subtype Classification, And Survival Prediction Using Radiology Images, Linmin Pei, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI). We first propose a 3D context aware deep learning, that considers uncertainty of tumor location in the radiology mMRI image sub-regions, to obtain tumor segmentation. We then apply a regular 3D convolutional neural network (CNN) on the tumor segments to achieve tumor subtype classification. Finally, we perform survival prediction …

Efficacy Of Radiomics And Genomics In Predicting Tp53 Mutations In Diffuse Lower Grade Glioma, Zeina A. Shboul, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

An updated classification of diffuse lower-grade gliomas is established in the 2016 World Health Organization Classification of Tumors of the Central Nervous System based on their molecular mutations such as TP53 mutation. This study investigates machine learning methods for TP53 mutation status prediction and classification using radiomics and genomics features, respectively. Radiomics features represent patients' age and imaging features that are extracted from conventional MRI. Genomics feature is represented by patients’ gene expression using RNA sequencing. This study uses a total of 105 LGG patients, where the patient dataset is divided into a training set (80 patients) and testing set …

Deep Learning With Context Encoding For Semantic Brain Tumor Segmentation And Patient Survival Prediction, Linmin Pei, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

One of the most challenging problems encountered in deep learning-based brain tumor segmentation models is the misclassification of tumor tissue classes due to the inherent imbalance in the class representation. Consequently, strong regularization methods are typically considered when training large-scale deep learning models for brain tumor segmentation to overcome undue bias towards representative tissue types. However, these regularization methods tend to be computationally exhaustive, and may not guarantee the learning of features representing all tumor tissue types that exist in the input MRI examples. Recent work in context encoding with deep CNN models have shown promise for semantic segmentation of …

Recovery From Visual Dysfunction Following Mild Traumatic Brain Injury Is Associated With Adaptive Reorganization Of Retinal Inputs To Lateral Geniculate Nucleus In The Mouse Model Utilizing Central Fluid Percussion Injury., Vishal C. Patel

Theses and Dissertations

Traumatic brain injury (TBI) is a leading cause of morbidity and mortality nationwide. Prevalence of mild TBI (mTBI) vastly outnumbers more severe forms however the associated morbidity has only recently gained public attention. Visual dysfunction is a significant component of mTBI associated morbidity with recovery of function linked with improvement in global outcomes. Examination of sensory and motor pathways in other brain injury paradigms support that recovery is largely dependent on adaptive plasticity of remaining connections. Current examinations of visual function recovery following mTBI is limited to identifying evidence for recovery and objective evidence for adaptive plasticity is limited. Therefore, …

Spinal Motor Neuron Excitability And Balance Control Changes Following Downslope Walking, Nikki Aitcheson-Huehn

Theses and Dissertations (Comprehensive)

Downslope walking (DSW) has been proposed as a rehabilitation tool for people with Multiple Sclerosis (PwMS) although there are mixed findings in young adults (YA) regarding the balance control changes, despite both populations experiencing depressed spinal motor neuron (MN) pool excitability. Our aim was to determine whether YAs could demonstrate improved balance control in conjunction with SOL H reflex depression (estimate of spinal MN excitability) following DSW. We also aimed to determine whether reciprocal inhibition was a potential mechanism for H reflex depression via conditioned SOL H reflexes. Thirty young adults (23±1.4y, 6 males) were assigned to 30-minutes of DSW …

Towards Making Videos Accessible For Low Vision Screen Magnifier Users, Ali Selman Aydin, Shirin Feiz, Vikas Ashok, Iv Ramakrishnan

Computer Science Faculty Publications

People with low vision who use screen magnifiers to interact with computing devices find it very challenging to interact with dynamically changing digital content such as videos, since they do not have the luxury of time to manually move, i.e., pan the magnifier lens to different regions of interest (ROIs) or zoom into these ROIs before the content changes across frames.

In this paper, we present SViM, a first of its kind screen-magnifier interface for such users that leverages advances in computer vision, particularly video saliency models, to identify salient ROIs in videos. SViM's interface allows users to zoom in/out …

Rotate-And-Press: A Non-Visual Alternative To Point-And-Click, Hae-Na Lee, Vikas Ashok, I. V. Ramakrishnan

Computer Science Faculty Publications

Most computer applications manifest visually rich and dense graphical user interfaces (GUIs) that are primarily tailored for an easy-and-efficient sighted interaction using a combination of two default input modalities, namely the keyboard and the mouse/touchpad. However, blind screen-reader users predominantly rely only on keyboard, and therefore struggle to interact with these applications, since it is both arduous and tedious to perform the visual 'point-and-click' tasks such as accessing the various application commands/features using just keyboard shortcuts supported by screen readers.

In this paper, we investigate the suitability of a 'rotate-and-press' input modality as an effective non-visual substitute for the visual …

Sail: Saliency-Driven Injection Of Aria Landmarks, Ali Selman Aydin, Shirin Feiz, Vikas Ashok, Iv Ramakrishnan

Computer Science Faculty Publications

Navigating webpages with screen readers is a challenge even with recent improvements in screen reader technologies and the increased adoption of web standards for accessibility, namely ARIA. ARIA landmarks, an important aspect of ARIA, lets screen reader users access different sections of the webpage quickly, by enabling them to skip over blocks of irrelevant or redundant content. However, these landmarks are sporadically and inconsistently used by web developers, and in many cases, even absent in numerous web pages. Therefore, we propose SaIL, a scalable approach that automatically detects the important sections of a web page, and then injects ARIA landmarks …