Bioimaging and Biomedical Optics Commons^™

Three-Dimensional (3d) Reconstruction Of Ultrasound Foetal Images Using Visualisation Toolkit (Vtk), Henry Dusim Grace Anabela

Student Works (2020-2029)

Three-dimensional (3D) reconstruction of two-dimensional (2D) ultrasound images has been used for easier analysing of anatomy related to abnormalities. 3D image reconstruction is required as it helps doctors interpret results and assess any defects quickly. Not only does it improve the accuracy of diagnosis, but it also saves a lot of time. In this thesis, 295 slices of 2D ultrasound images of a foetal are used to do 3D reconstruction using surface rendering, volume rendering, and multiplanar rendering technique. A similar software known as 3D Slicer is also used to reconstruct the ultrasound image for comparison purpose. VTK can easily …

Identification Of Microscopy Cell Images By Using Convolutional Neural Network Application, Ajis Norfatin Farisya

Student Works (2020-2029)

Breast cancer has been the major factor of cancer death and the second main cause of women’s deaths in the world. The false positive results of this cancer cell detection during the screening test leads to false treatment and emotional disturbance of the patients. Thus, breast cancer cell lines (MCF7) is used as the microscopy image samples together with the Human Bone Osteosarcoma Epithelial Cells (U2OS), and Human Hepatocyte as control to study the effectiveness of convolutional neural network (CNN) as a method of image recognition. The objectives of this study are to determine the ability of convolutional neural network …

An Improved Bone Age Assessment Using Advanced Image Processing And Deep Learning Approach, Kim Meng Liang

Student Works (2020-2029)

Pediatricians often apply bone age assessment to measure the skeletal maturity of children and to predict the future height. These discrepancies are good indicators for diagnosing growth disorders. Normally, left hand skeletal is employed in this assessment. The low quality of ossification sites of carpals deteriorates the pediatrician’s visibility in inspecting the pertinent radiographic manifestations. This in turn affects the bone age assessment. Therefore, we have to enhance the quality before assessing them. Histogram equalization is one of the contrast enhancement techniques that suit this type of enhancement. Existing histogram equalizations, however, are confronting with problems in preserving the brightness …

Active Deep Learning Method To Automate Unbiased Stereology Cell Counting, Saeed Alahmari

USF Tampa Graduate Theses and Dissertations

Cell quantification in histopathology images plays a significant role in understanding and diagnosing diseases such as cancer and Alzheimers. The gold-standard for quantifying cells in tissue sections is the unbiased stereology approach. Unfortunately, in unbiased stereology current practices rely on a well-trained human to manually count hundreds of cells in microscopy images. However, this human-based manual approach is time-consuming, labor-intensive, subject to human errors, recognition bias, fatigue, variable training, poor reproducibility, and inter-observer error. Thus, the lack of high-throughput technology for automating unbiased stereology analyses remains a major obstacle to further progress in a wide range of neuroscience and cancer …

Brain Structure Changes Over Time In Normal And Mildly Impaired Aged Persons, Charles D. Smith, Linda J. Van Eldik, Gregory A. Jicha, Frederick A. Schmitt, Peter T. Nelson, Erin L. Abner, Richard J. Kryscio, Richard R. Murphy, Anders H. Andersen

Neurology Faculty Publications

Structural brain changes in aging are known to occur even in the absence of dementia, but the magnitudes and regions involved vary between studies. To further characterize these changes, we analyzed paired MRI images acquired with identical protocols and scanner over a median 5.8-year interval. The normal study group comprised 78 elders (25M 53F, baseline age range 70-78 years) who underwent an annual standardized expert assessment of cognition and health and who maintained normal cognition for the duration of the study. We found a longitudinal grey matter (GM) loss rate of 2.56 ± 0.07 ml/year (0.20 ± 0.04%/year) and a …

Lung Nodule Malignancy Prediction From Computed Tomography Images Using Deep Learning, Rahul Paul

USF Tampa Graduate Theses and Dissertations

Lung cancer has a high incidence and mortality rate. The five-year relative survival rate for all lung cancers is 18%. Due to the high mortality and incidence rate of lung cancer worldwide, early detection is essential. Low dose Computed Tomography (CT) is a commonly used technique for screening, diagnosis, and prognosis of non-small cell lung cancer (NSCLC). The National Lung Screening Trial (NLST) compared low-dose helical computed tomography (LDCT) and standard chest radiography (CXR) for three annual screens and reported a 20% relative reduction in lung cancer mortality for LDCT compared to CXR. As such, LDCT screening for lung cancer …

Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing, Eric Daniel Morris

Wayne State University Dissertations

Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment.

Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup …

Deep Learning For Digitized Histology Image Analysis, Sudhir Sornapudi

Doctoral Dissertations

“Cervical cancer is the fourth most frequent cancer that affects women worldwide. Assessment of cervical intraepithelial neoplasia (CIN) through histopathology remains as the standard for absolute determination of cancer. The examination of tissue samples under a microscope requires considerable time and effort from expert pathologists. There is a need to design an automated tool to assist pathologists for digitized histology slide analysis. Pre-cervical cancer is generally determined by examining the CIN which is the growth of atypical cells from the basement membrane (bottom) to the top of the epithelium. It has four grades, including: Normal, CIN1, CIN2, and CIN3. In …

Gravity-Drawing Flexible Silicone Filaments As Fiber Optics And Model Foldamers, Katherine Snell

CMC Senior Theses

Here, we present a method of gravity-drawing polydimethylsiloxane (PDMS) silicone fibers with application as fiber optics and as model foldamers. Beginning as a viscous liquid, PDMS is cured using heat until its measured viscosity reaches 4000 mPa•s. The semi-cured elastomer is then extruded through a tube furnace to produce thin (diameters on the order of hundred micrometers) filaments with scalable lengths. PDMS is biocompatible, gas-permeable, flexible, and hydrophobic. Additionally, the PDMS surface hydrophobicity can be modified via UV exposure, O2 plasma, and corona discharge. We demonstrate the patternibility (i.e patterns of hydrophobicity) of PDMS fibers, adding complexity to potential foldamer …

Automatic Methods To Enhance The Quality Of Colonoscopy Video, Nidhal Kareem Shukur Azawi

Graduate Theses and Dissertations

Colonoscopy is a form of endoscopy because it uses colonoscopy device to help the doctor to understand a colon patient. Enhancing the quality of Colonoscopy images is a challenge because of the wet and dynamic environment inside the colon causes many problems even the colonoscope devise has a good quality. Some of these problems are blurriness, specular highlights shiny areas.

In this work, different kinds of techniques have been investigated in order to improve the quality of colonoscopy images. Also, variety of preprocessing approaches (removing bad images, resizing images, median filtration with and without image resizing) have been conducted to …

Using Feature Extraction From Deep Convolutional Neural Networks For Pathological Image Analysis And Its Visual Interpretability, Wei-Wen Hsu

Electrical & Computer Engineering Theses & Dissertations

This dissertation presents a computer-aided diagnosis (CAD) system using deep learning approaches for lesion detection and classification on whole-slide images (WSIs) with breast cancer. The deep features being distinguishing in classification from the convolutional neural networks (CNN) are demonstrated in this study to provide comprehensive interpretability for the proposed CAD system using the domain knowledge in pathology. In the experiment, a total of 186 slides of WSIs were collected and classified into three categories: Non-Carcinoma, Ductal Carcinoma in Situ (DCIS), and Invasive Ductal Carcinoma (IDC). Instead of conducting pixel-wise classification (segmentation) into three classes directly, a hierarchical framework with the …

Seeing Eye To Eye: A Machine Learning Approach To Automated Saccade Analysis, Maigh Attre

Honors Scholar Theses

Abnormal ocular motility is a common manifestation of many underlying pathologies particularly those that are neurological. Dynamics of saccades, when the eye rapidly changes its point of fixation, have been characterized for many neurological disorders including concussions, traumatic brain injuries (TBI), and Parkinson’s disease. However, widespread saccade analysis for diagnostic and research purposes requires the recognition of certain eye movement parameters. Key information such as velocity and duration must be determined from data based on a wide set of patients’ characteristics that may range in eye shapes and iris, hair and skin pigmentation [36]. Previous work on saccade analysis has …

Quantifying Iron Overload Using Mri, Active Contours, And Convolutional Neural Networks, Andrea Sajewski, Stacey Levine

Undergraduate Research and Scholarship Symposium

Iron overload, a complication of repeated blood transfusions, can cause tissue damage and organ failure. The body has no regulatory mechanism to excrete excess iron, so iron overload must be closely monitored to guide therapy and measure treatment response. The concentration of iron in the liver is a reliable marker for total body iron content and is now measured noninvasively with magnetic resonance imaging (MRI). MRI produces a diagnostic image by measuring the signals emitted from the body in the presence of a constant magnetic field and radiofrequency pulses. At each pixel, the signal decay constant, T2*, can be calculated, …

3d Microwave Tomography Using The Soft Prior Regularization Technique: Evaluation In Anatomically-Realistic Mri-Derived Numerical Breast Phantoms, Amir H. Golnabi, Paul M. Meaney, Shireen D. Geimer, Keith D. Paulsen

Department of Mathematics Faculty Scholarship and Creative Works

Objective: Fusion of magnetic resonance imaging (MRI) breast images with microwave tomography is accomplished through a soft prior technique, which incorporates spatial information (from MRI), i.e. accurate boundary location of different regions of interest, into the regularization process of the microwave image reconstruction algorithm. Methods: Numerical experiments were completed on a set of 3D breast geometries derived from MR breast data with different parenchymal densities, as well as a simulated tumor to evaluate performance over a range of breast shapes, sizes and property distributions. Results: When the soft prior regularization technique was applied, both permittivity and conductivity relative root mean …

Abso2luteu-Net: Tissue Oxygenation Calculation Using Photoacoustic Imaging And Convolutional Neural Networks, Kevin Hoffer-Hawlik, Geoffrey P. Luke

ENGS 88 Honors Thesis (AB Students)

Photoacoustic (PA) imaging uses incident light to generate ultrasound signals within tissues. Using PA imaging to accurately measure hemoglobin concentration and calculate oxygenation (sO₂) requires prior tissue knowledge and costly computational methods. However, this thesis shows that machine learning algorithms can accurately and quickly estimate sO₂. absO₂luteU-Net, a convolutional neural network, was trained on Monte Carlo simulated multispectral PA data and predicted sO₂ with higher accuracy compared to simple linear unmixing, suggesting machine learning can solve the fluence estimation problem. This project was funded by the Kaminsky Family Fund and the Neukom Institute.

End-To-End Learning Via A Convolutional Neural Network For Cancer Cell Line Classification, Darlington A. Akogo, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

Purpose: Computer vision for automated analysis of cells and tissues usually include extracting features from images before analyzing such features via various machine learning and machine vision algorithms. The purpose of this work is to explore and demonstrate the ability of a Convolutional Neural Network (CNN) to classify cells pictured via brightfield microscopy without the need of any feature extraction, using a minimum of images, improving work-flows that involve cancer cell identification.

Design/methodology/approach: The methodology involved a quantitative measure of the performance of a Convolutional Neural Network in distinguishing between two cancer lines. In their approach, they trained, validated and …

Optical Vortex And Poincaré Analysis For Biophysical Dynamics, Anindya Majumdar

Dissertations, Master's Theses and Master's Reports

Coherent light - such as that from a laser - on interaction with biological tissues, undergoes scattering. This scattered light undergoes interference and the resultant field has randomly added phases and amplitudes. This random interference pattern is known as speckles, and has been the subject of multiple applications, including imaging techniques. These speckle fields inherently contain optical vortices, or phase singularities. These are locations where the intensity (or amplitude) of the interference pattern is zero, and the phase is undefined.

In the research presented in this dissertation, dynamic speckle patterns were obtained through computer simulations as well as laboratory setups …

Applications Of Machine Learning In Nuclear Imaging And Radiation Detection, Shaikat Mahmood Galib

Doctoral Dissertations

"The main focus of this work is to use machine learning and data mining techniques to address some challenging problems that arise from nuclear data. Specifically, two problem areas are discussed: nuclear imaging and radiation detection. The techniques to approach these problems are primarily based on a variant of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN), which is one of the most popular forms of 'deep learning' technique.

The first problem is about interpreting and analyzing 3D medical radiation images automatically. A method is developed to identify and quantify deformable image registration (DIR) errors from lung CT scans …

In Vivo Vascular Imaging With Photoacoustic Microscopy, Hsun-Chia Hsu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Photoacoustic (PA) tomography (PAT) has received extensive attention in the last decade for its capability to provide label-free structural and functional imaging in biological tissue with highly scalable spatial resolution and penetration depth. Compared to modern optical modalities, PAT offers speckle-free images and is more sensitive to optical absorption contrast (with 100% relative sensitivity). By implementing different regimes of optical wavelength, PAT can be used to image diverse light-absorbing biomolecules. For example, hemoglobin is of particular interest in the visible wavelength regime owing to its dominant absorption, and lipids and water are more commonly studied in the near-infrared regime.

In …

Super‐Resolution Imaging Of Amyloid Structures Over Extended Times By Using Transient Binding Of Single Thioflavin T Molecules, Kevin Spehar, Tianben Ding, Yuanzi Sun, Niraja Kedia, Jin Lu, George R. Nahass, Matthew D. Lew, Jan Bieschke

Electrical & Systems Engineering Publications and Presentations

Oligomeric amyloid structures are crucial therapeutic targets in Alzheimer's and other amyloid diseases. However, these oligomers are too small to be resolved by standard light microscopy. We have developed a simple and versatile tool to image amyloid structures by using thioflavin T without the need for covalent labeling or immunostaining. The dynamic binding of single dye molecules generates photon bursts that are used for fluorophore localization on a nanometer scale. Thus, photobleaching cannot degrade image quality, allowing for extended observation times. Super‐resolution transient amyloid binding microscopy promises to directly image native amyloid by using standard probes and record amyloid dynamics …

Developing Wavefront Shaping Techniques For Focusing Through Highly Dynamic Scattering Media, Ashton Hemphill

McKelvey School of Engineering Graduate Student Theses & Dissertations

One of the prime limiting factors of optical imaging in biological applications is the diffusion of light by tissue, which prevents focusing at depths greater than the optical diffusion limit of ~1 mm in soft tissue. This greatly restricts the utility of optical diagnostic and therapeutic techniques, such as optogenetics, microsurgery, optical tweezing, and phototherapy of deep tissue, which require focused light in order to function. Wavefront shaping extends the depth at which optical focusing may be achieved by compensating for phase distortions induced by scattering, allowing for focusing through constructive interference.

However, due to physiological motion, scattering of light …

Fluorescence Guided Tumor Imaging: Foundations For Translational Applications, Jessica P. Miller

McKelvey School of Engineering Graduate Student Theses & Dissertations

Optical imaging for medical applications is a growing field, and it has the potential to improve medical outcomes through its increased sensitivity and specificity, lower cost, and small instrumentation footprint as compared to other imaging modalities. The method holds great promise, ranging from direct clinical use as a diagnostic or therapeutic tool, to pre-clinical applications for increased understanding of pathology. Additionally, optical imaging uses non-ionizing radiation which is safe for patients, so it can be used for repeated imaging procedures to monitor therapy, guide treatment, and provide real-time feedback. The versatile features of fluorescence-based optical imaging make it suited for …

Developing Photoacoustic Tomography Devices For Translational Medicine And Basic Science Research, Tsz Wai Wong

McKelvey School of Engineering Graduate Student Theses & Dissertations

Photoacoustic (PA) tomography (PAT) provides volumetric images of biological tissue with scalable spatial resolutions and imaging depths, while preserving the same imaging contrast—optical absorption. Taking the advantage of its 100% sensitivity to optical absorption, PAT has been widely applied in structural, functional, and molecular imaging, with both endogenous and exogenous contrasts, at superior depths than pure optical methods. Intuitively, hemoglobin has been the most commonly studied biomolecule in PAT due to its strong absorption in the visible wavelength regime.

One of the main focuses of this dissertation is to investigate an underexplored wavelength regime—ultraviolet (UV), which allows us to image …

An Area Based Fan Beam Projection Model, Richard E. Steele, Jiehua Zhu

Honors College Theses

Area based projection models for computed tomography mitigate raw data errors by treating X-Rays as beams, whereas traditional line based projection models treat an X-Ray like a line, thus generating significant error. In an existing area based fan beam projection model, a rotation matrix, Q, simulates the rotation of the emitter detector pair to reduce computational load, but this introduces approximations by using an approximated rotation matrix. We eliminate approximations by deriving an exact formula for the entries of Q. Using a rotation of axes and by considering the neighboring cells' contributions to the area, the result has formulations for …

Characterization Of Computed Tomography Radiomic Features Using Texture Phantoms, Muhammad Shafiq Ul Hassan

USF Tampa Graduate Theses and Dissertations

Radiomics treats images as quantitative data and promises to improve cancer prediction in radiology and therapy response assessment in radiation oncology. However, there are a number of fundamental problems that need to be solved in order to potentially apply radiomic features in clinic. The first basic step in computed tomography (CT) radiomic analysis is the acquisition of images using selectable image acquisition and reconstruction parameters. Radiomic features have shown large variability due to variation of these parameters. Therefore, it is important to develop methods to address these variability issues in radiomic features due to each CT parameter. To this end, …

Tracing Actin Filament Bundles In Three-Dimensional Electron Tomography Density Maps Of Hair Cell Stereocilia, Salim Sazzed, Junha Song, Julio Kovacs, Willi Wriggers, Manfred Auer, Jing He

Computer Science Faculty Publications

Cryo-electron tomography (cryo-ET) is a powerful method of visualizing the three-dimensional organization of supramolecular complexes, such as the cytoskeleton, in their native cell and tissue contexts. Due to its minimal electron dose and reconstruction artifacts arising from the missing wedge during data collection, cryo-ET typically results in noisy density maps that display anisotropic XY versus Z resolution. Molecular crowding further exacerbates the challenge of automatically detecting supramolecular complexes, such as the actin bundle in hair cell stereocilia. Stereocilia are pivotal to the mechanoelectrical transduction process in inner ear sensory epithelial hair cells. Given the complexity and dense arrangement of actin …

System Optimization And Iterative Image Reconstruction In Photoacoustic Computed Tomography For Breast Imaging, Yang Lou

McKelvey School of Engineering Graduate Student Theses & Dissertations

Photoacoustic computed tomography(PACT), also known as optoacoustic tomography (OAT), is an emerging imaging technique that has developed rapidly in recent years. The combination of the high optical contrast and the high acoustic resolution of this hybrid imaging technique makes it a promising candidate for human breast imaging, where conventional imaging techniques including X-ray mammography, B-mode ultrasound, and MRI suffer from low contrast, low specificity for certain breast types, and additional risks related to ionizing radiation. Though significant works have been done to push the frontier of PACT breast imaging, it is still challenging to successfully build a PACT breast imaging …

Hierarchical Fusion Based Deep Learning Framework For Lung Nodule Classification, Kazim Sekeroglu

LSU Doctoral Dissertations

Lung cancer is the leading cancer type that causes the mortality in both men and women. Computer aided detection (CAD) and diagnosis systems can play a very important role for helping the physicians in cancer treatments. This dissertation proposes a CAD framework that utilizes a hierarchical fusion based deep learning model for detection of nodules from the stacks of 2D images. In the proposed hierarchical approach, a decision is made at each level individually employing the decisions from the previous level. Further, individual decisions are computed for several perspectives of a volume of interest (VOI). This study explores three different …

Computational Modeling For Abnormal Brain Tissue Segmentation, Brain Tumor Tracking, And Grading, Syed Mohammad Shamin Reza

Electrical & Computer Engineering Theses & Dissertations

This dissertation proposes novel texture feature-based computational models for quantitative analysis of abnormal tissues in two neurological disorders: brain tumor and stroke. Brain tumors are the cells with uncontrolled growth in the brain tissues and one of the major causes of death due to cancer. On the other hand, brain strokes occur due to the sudden interruption of the blood supply which damages the normal brain tissues and frequently causes death or persistent disability. Clinical management of these brain tumors and stroke lesions critically depends on robust quantitative analysis using different imaging modalities including Magnetic Resonance (MR) and Digital Pathology …

Chelator-Free Radiolabeling Of Serrs Nanoparticles For Whole-Body Pet And Intraoperative Raman Imaging, Matthew A. Wall, Travis Shaffer, Stefan Harmsen, Darjus-Felix Tschaharganeh, Chun-Hao Huang, Scott W. Lowe, Charles Michael Drain, Moritz F. Kircher

Publications and Research

A single contrast agent that offers whole-body non-invasive imaging along with the superior sensitivity and spatial resolution of surface-enhanced resonance Raman scattering (SERRS) imaging would allow both pre-operative mapping and intraoperative imaging and thus be highly desirable. We hypothesized that labeling our recently reported ultrabright SERRS nanoparticles with a suitable radiotracer would enable pre-operative identification of regions of interest with whole body imaging that can be rapidly corroborated with a Raman imaging device or handheld Raman scanner in order to provide high precision guidance during surgical procedures. Here we present a straightforward new method that produces radiolabeled SERRS nanoparticles for …