Building a surface atlas of hippocampal subfields from high resolution T2-weighted MRI scans using landmark-free surface registration

The hippocampus is widely studied in neuroimaging field as it plays important roles in memory and learning. However, the critical subfield information is often not explored in most hippocampal studies. We previously proposed a method for hippocampal subfield morphometry by integrating FreeSurfer, FSL, and SPHARM tools. But this method had some limitations, including the analysis of T1-weighted MRI scans without detailed subfield information and hippocampal registration without using important subfield information. To bridge these gaps, in this work, we propose a new framework for building a surface atlas of hippocampal subfields from high resolution T2-weighted MRI scans by integrating state-of-the-art methods for automated segmentation of hippocampal subfields and landmark-free, subfield-aware registration of hippocampal surfaces. Our experimental results have shown the promise of the new framework

Segmentation of fluorescence microscopy images using three dimensional active contours with inhomogeneity correction

Image segmentation is an important step in the quantitative analysis of fluorescence microscopy data. Since fluorescence microscopy volumes suffer from intensity inhomogeneity, low image contrast and limited depth resolution, poor edge details, and irregular structure shape, segmentation still remains a challenging problem. This paper describes a nuclei segmentation method for fluorescence microscopy based on the use of three dimensional (3D) active contours with inhomogeneity correction. The correction information utilizes 3D volume information while addressing intensity inhomogeneity across vertical and horizontal directions. Experimental results demonstrate that the proposed method achieves better performance than other reported methods

Electromagnetic Simulation of Non-Invasive Approach for the Diagnosis of Diabetic Foot Ulcers

Diabetic foot ulcers are systemic diseases that affect all blood vessels within the human body. From major blood vessels to microvasculature, hardening, thickening, and narrowing of blood vessels ultimately results to diminished blood flow to end organs. The detrimental effects of peripheral vascular disease are well recognized across medicine, particularly with regards to diabetic foot ulcers. Diabetic foot ulcers (DFU) are common across all fields of medicine, including but not limited to: orthopedics, vascular surgery, podiatry, general internal medicine, and infectious disease. As the population of the United States continues to grow in age and obesity, diabetes and DFU are becoming more and more prevalent in our medical society. Current approaches to diagnosing peripheral vascular disease ultimately result in some degree of invasiveness for the patient. Preliminary lab studies, such as the ankle-brachial index and Doppler ultrasound of peripheral arteries, provide efficient safe screening methods. However, these studies lack quantification of the degree of vascular stenosis and are unable to accurately assess the location of narrowing. In current practice, radiologists are called upon to for angiography of the blood vessels using contrast dye. This provides an additional risk for diabetic patients: a population inherently at risk for renal disease. In this study, we proposed utilizing electromagnetic simulation with boundary conditions set at various layers of human tissues. More specifically, the human foot was analyzed using COMSOL multi-physics software in attempt to visualize, analyze, and quantify the degree of peripheral vascular disease, which plays a pivotal role in the development of diabetic foot ulcers. The simulation was conducted for a patient’s foot, with bone, blood vessels, and surrounding fat layers to emulate the anatomy of a diabetic foot. A 2-D scan was obtained to assess and visualize the blood vessel’s narrowing, widening, vascular turbulence, or occlusion. The analysis was conducted at two frequencies, 2 GHz and 5 GHz, and compared to one another to assess the accuracy of clinical diagnosis. An electric field was generated throughout the 2D model at 20, 50, and 100 Joules, respectively. The simulation was able to adequately predict and stratify varying degrees of occlusion within peripheral vasculature. This study, though a simulation in nature, shows promise for being able to accurately diagnose the peripheral vasculature using electromagnetic parameters. This feasibility study proved successful for possible future implementation using MEMS/NEMS device systems to be designed to detect EM parameters to serve as a diagnostic tool for the early detection of peripheral vascular disease, and ultimately, diabetic foot ulcers

Supporting Teaching Excellence and Scholarship

Funding Proposal for the IUPUI Mentoring AcademyOne main focus of the School of Engineering and Technology’s current strategic plan is to “excel in the delivery of instruction, the scholarship of teaching and learning . . . to support extraordinary student success.” And while the School of Engineering and Technology has a long history of teaching excellence most mentorship activities focus on supporting faculty seeking excellence in research/discovery. According to the 2015 campus faculty professional development satisfaction survey over a third (34.5%) of tenured and tenure-track E & T faculty, a quarter (25%) of full-time non-tenure track faculty, and two-thirds (67%) of part-time and adjunct faculty rated their satisfaction with professional development opportunities related to teaching as either only “somewhat satisfied” or “not satisfied”. Furthermore, approximately 40% of E & T non-tenure track and tenure track faculty are not satisfied or only somewhat satisfied with available mentoring opportunities. Thus, it is apparent there is a need to develop an intentional, sustainable program focused on developing faculty capacity for scholarship in teaching while providing mentoring and leadership opportunities for mid-career faculty. This proposal describes the structure and programming to provide a robust climate for the testing, integration, and dissemination of pedagogical practices in engineering and technology. The proposed programming leverages available campus resources and expertise, as well as a strengthening of current programming. Six Engineering and Technology faculty have agreed to be paired with faculty interested in focusing their scholarship in teaching and learning. Individual mentoring sessions, tailored to the mentees’ needs, will occur throughout the academic year. Monthly workshops (currently called “Lunch & Learn”) will cover discipline-specific topics related to pedagogy and learning. The program will be assessed on three different levels: participant satisfaction, assessment of teaching scholarship, and adoption of best practices. Our goal is to create a culture within the School that explicitly values innovative student-centered teaching and related dissemination

Tubule Segmentation of Fluorescence Microscopy Images Based on Convolutional Neural Networks With Inhomogeneity Correction

Fluorescence microscopy has become a widely used tool for studying various biological structures of in vivo tissue or cells. However, quantitative analysis of these biological structures remains a challenge due to their complexity which is exacerbated by distortions caused by lens aberrations and light scattering. Moreover, manual quantification of such image volumes is an intractable and error-prone process, making the need for automated image analysis methods crucial. This paper describes a segmentation method for tubular structures in fluorescence microscopy images using convolutional neural networks with data augmentation and inhomogeneity correction. The segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods. Experimental results indicate that the proposed method has better performance with correctly segmenting and identifying multiple tubular structures compared to other methods