Bioimaging and Biomedical Optics Commons^™

Automated Beam Stitching And Segmentation Procedure For Space Division Multiplexing Optical Coherence Tomography Angiography, Andrew J. Song

McKelvey School of Engineering Graduate Student Theses & Dissertations

Optical Coherence Tomography Angiography (OCTA) has revolutionized ophthalmic imaging and its capability to produce high-resolution 3D maps of the retinal microvasculature is instrumental in diagnosing retinovascular diseases such as diabetic retinopathy and age-related macular degeneration; However, the existing OCTA devices often suffer from slow acquisition speed limiting the field-of-view (FOV) in the clinic. Space Division Multiplexing OCTA (SDM-OCTA) address these limitations by acquiring multiple beams simultaneously, achieving manyfold faster acquisition speeds than single beam OCTA systems. But as each beam contains only part of the image, SDM-OCTA requires additional processing steps to produce coherent wide-field images. Though manual stitching and …

Development Of Translational Microscale Systems To Interrogate How Biophysical And Biochemical Cues Alter The Phenotype Of Hormone Positive Breast Cancer, Braulio Ortega Quesada

All Dissertations

Cancer is a complicated disease and one in every three diagnoses are women with breast cancer. Recent research shows that the progression of cancer depends strongly on interactions with other non-cancerous cells and non-cellular components. Metastasis, the spread of cancer to other parts of the body, is a key step in cancer progression and makes treatment harder, leading to poor response to treatments culminating in death in ~90% of patients. This dissertation focused on creating a series of microdevices as novel pre-clinical models of breast cancer. Two different devices were fabricated to (1) study how exposing cancer cells to the …

Deep Learning In Lung Cancer Pre- And Post-Radiation Therapy: Diagnosis Of Malignancy And Radiation-Induced Lung Injury From 3d X-Ray Ct., Benjamin Peter Veasey

Electronic Theses and Dissertations

Lung cancer remains the leading cause of cancer-related mortality worldwide, with early detection and accurate diagnosis being critical for improving patient outcomes. Additionally, the progression of Radiation-Induced Lung Injury (RILI) following Stereotactic Body Radiation Therapy (SBRT) for lung cancer presents a significant diagnostic challenge. This dissertation addresses these challenges by developing deep learning-based diagnostic tools for both pre-treatment lung nodule malignancy classification and post-treatment RILI identification using 3D X-ray CT imaging. The research is divided into two primary objectives. First, for lung nodule malignancy classification, we developed a biopsy-confirmed dataset, called NLSTx, to train and evaluate deep learning models while …

Impact Of Shear Flow On Marine Biofilms Using Hyperspectral Imaging, Shantanu Mahesh Kore

All Theses

Various microbial communities in marine biofilms cause biofouling on submerged surfaces, posing challenges to marine industries. Despite their ecological and economic importance, biofilms' biomechanical and biochemical responses to hydrodynamic shear stress are insufficiently understood, particularly in dynamic flow conditions. To fill this gap, our study uses an innovative fiber-optic hyperspectral imaging (HSI) system and a supercontinuum laser source to examine marine biofilms' structure, composition, and detachment behavior at different shear stress levels.

To detect spectral and spatial heterogeneity in live biofilms without labeling, we created a custom imaging pipeline that captures reflectance spectra in the 600-850 nm range, targeting microbial …

Painting Rich Six-Dimensional Pictures Using Polarized Fluorescence Microscopy, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

No abstract provided.

Multimodal Ai-Driven Biomarker For Early Detection Of Cancer Cachexia, Sabeen Ahmed

USF Tampa Graduate Theses and Dissertations

Cancer cachexia is a metabolic syndrome characterized by substantial skeletal muscle loss, impacting cancer patients' survival and quality of life. Despite its clinical significance, early detection remains a challenge due to the lack of standardized diagnostic criteria and the reliance on indirect markers. This work presents an AI-driven approach to enhance cachexia detection and monitoring by integrating multiple deep learning methodologies. We explore transformer architectures for time-series analysis to model sequential medical data, enabling disease prediction and progression modeling. To ensure robust and reliable decision-making in clinical settings, we explore Bayesian deep neural networks for uncertainty estimation. Additionally, we introduce …

Investigating Spatial Mapping Of Lipid And Protein Alterations Within Cells And Tissues Using Raman Microspectroscopy, Elnaz Sheikh

LSU Doctoral Dissertations

The progression of various diseases is associated with alterations in the microenvironment of cells, consisting of a variety of metabolites like lipids and proteins. It is well known that lipids and proteins are potential biomarkers for indicating alterations within the cell microenvironment induced by disease, and various methods are frequently used for their examination. To understand the chemical composition and spatial distribution of constituents in tissues and cells, confocal Raman spectroscopy and microscopy, as non-destructive tools, have shown potential for monitoring abnormalities at the cellular level with high resolution and can facilitate detection through targeting multiple biomarkers. The application of …

Unified Adaptive Cross-Attention Multimodal Network, Mahesh Sunuwar

Shelby Hall Graduate Research Forum Posters

Accurately diagnosing appendicitis remains a significant challenge in emergency medicine due to its varied clinical presentation and overlap with other abdominal conditions. Existing diagnostic models often rely on unimodal data, such as computed tomography (CT) images or clinical notes, limiting their ability to capture the full complexity of patient information. This study proposes a novel multimodal framework, a Unified Adaptive Cross-Attention Multimodal Framework (u-ACM), that integrates CT images and clinical notes using hybrid fusion strategies to improve diagnostic accuracy for appendicitis. The u-ACM leverages adaptive contextual filtering to dynamically remove irrelevant features, crossattention mechanisms to align features between modalities, and …

Explorations Of Dna-Single-Walled Carbon Nanotube Interactions To Develop Multiplexed Molecularly Specific Biosensors For Inflammation, Amelia K. Ryan

Dissertations and Theses

Inflammatory cytokines such as interleukin-6 (IL-6) and interleukin-12 (IL-12) are central regulators of immune signaling and key biomarkers of disease, yet existing assays for their detection remain slow, invasive, and lack multiplexing ability. This dissertation advances the development of single-walled carbon nanotube (SWCNT) optical nanosensors capable of real-time, multiplexed, and molecularly specific cytokine detection through innovative use of single-stranded DNA (ssDNA) interfaces.

First, an IL-6-specific DNA aptamer was employed as both a dispersing agent and recognition probe for SWCNT fluorescence sensing. Sequence modifications, including anchor domains, truncations, and thermally induced refolding, were systematically tested to optimize sensitivity and selectivity. The …

Advancing Electrical Stimulation: Full-Head Mri Segmentation For Abnormal Brain Anatomy With Tdcs, Andrew Birnbaum

Dissertations and Theses

Evaluating the effectiveness of transcranial direct current stimulation (tDCS) is essential for guiding its integration into therapeutic and performance-enhancement applications. In our laboratory, we investigate the efficacy of tDCS across multiple experimental models, including both animal and human studies. I have contributed significantly to the execution and analysis of these experiments, which include studies in rats and healthy human participants aimed at evaluating whether electrical stimulation of the motor cortex can enhance motor learning. These studies assess improvements in fine motor performance resulting from tDCS. In stroke patients, I contribute to our investigation of tDCS as a rehabilitative intervention, particularly …

Optimal Tissue Clearing Of Gastric Cancerous Organoids Imaged Via High Resolution Light Sheet Microscopy, Lauren D. Lieu

2025 Fall Honors Capstones Projects - Archive

While the rate of surviving a myocardial infarction has increased in the past 60 years from 60% to 90%, the rate of Americans developing chronic heart disease such as heart failure, arrhythmias, and hypertensive heart disease has risen. This has led to the development of imaging technology such as Light Sheet Microscopy (LSM) and Light Field Microscopy (LFM) to study heart contractility and congenital heart disease via models such as cancerous organoids or Zebrafish. However, LSM and LFM results are highly dependent on the quality of the staining and cleared samples. This study looks to add to the growing field …

Optical Methods For Time-Resolved Dosimetry And Oximetry Of Ultra-High Dose Rate Radiation Therapy, Megan A. Clark

Dartmouth College Ph.D Dissertations

Radiotherapy (RT) is a cornerstone method used to treat over 50% of the 2 million new cancer diagnoses each year in the United States. The success of RT directly relies on an optimal balance between maximizing the dose to the tumor while minimizing dose to surrounding normal tissues. Achieving this balance is often challenging due to underlying radiation transport and the presence of anatomical constraints that limit the beam delivery geometry. In turn, minimizing healthy tissue toxicity prevents use of a more aggressive tumor killing approach, and even in curative cases may decrease the patient’s quality of life due to …

Emotion Processing In Adolescents With Epilepsy And Healthy Controls Using Multi-Modal Neuroimaging, Frances Kathryn E. King

Bioengineering Dissertations - Archive

Epilepsy is increasingly recognized as a disorder not only of seizures but also of widespread network dysfunction, impacting cognitive and emotional processing. One critical cognitive process affected in epilepsy is emotional conflict processing, which relies on the interplay of multiple brain networks. However, the spatiotemporal dynamics of emotional conflict processing in epilepsy, particularly in adolescents, have remained poorly understood. This dissertation investigated the neural mechanisms underlying emotional conflict processing in typically developing adolescents and adolescents with epilepsy using magnetoencephalography (MEG). The first study elucidated the spatiotemporal profile of emotional conflict processing in 24 typically developing adolescents using magnetoencephalography (MEG). Cluster-based …

Multimodal Imaging Of Protein-Based Biomaterials For Delivering Chemo-Agent Cargo For Glioblastoma Treatment In A Murine Model, Orin Mishkit

Dissertations and Theses

Protein-based self-assembling biomaterials, known as Thermo-Responsive Assembled Proteins (TRAP), present meaningful potential as drug delivery carriers. These materials enable the controlled, slow release of poorly soluble chemotherapeutic agents, such as doxorubicin (Dox), while potentially reducing systemic off-target effects. In collaboration with multiple NYU labs, this study evaluated the efficacy of two TRAP variants—TRAP and F-TRAP—as drug delivery carriers in a xenograft mouse model of glioblastoma multiforme (GBM).

The primary objective was to compare the effectiveness of TRAP-loaded Dox (TRAP-DOX) to free Dox in achieving tumor extravasation and accumulation, facilitating sustained drug release. We hypothesized that the leaky vasculature of GBM …

Generation And Characterization Of Human Blood-Brain Barrier Models For Investigating Neuropsychiatric Disorders And Tumor Metastasis, Yunfei Li

Dissertations and Theses

The blood–brain barrier (BBB) is important in the normal function of the central nervous system (CNS). An altered BBB has been described in various neuropsychiatric disorders. The brain-specific microvascular endothelial cell (BMEC) is an essential structural component of the BBB. To test if integrity of the BBB formed by BMECs is compromised in 22q11.2 deletion syndrome (also called DiGeorge syndrome), which is one of the validated genetic risk factors for schizophrenia, a 2D iBBB (induced BBB) on a Transwell filter was generated from human microvascular endothelial cells (HBMECs) derived from the induced pluripotent stem cells (iPSCs) out of patients with …

Adaptive Fusion Neural Networks For Sparse-Angle X-Ray 3d Reconstruction, Shaoyong Hong, Bo Yang, Yan Chen, Hao Quan, Shan Liu, Minyi Tang, Jiawei Tian

Electrical & Computer Engineering Faculty Publications

3D medical image reconstruction has significantly enhanced diagnostic accuracy, yet the reliance on densely sampled projection data remains a major limitation in clinical practice. Sparse-angle X-ray imaging, though safer and faster, poses challenges for accurate volumetric reconstruction due to limited spatial information. This study proposes a 3D reconstruction neural network based on adaptive weight fusion (AdapFusionNet) to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images. To address the issue of spatial inconsistency in multi-angle image reconstruction, an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and perform weighted fusion, thereby improving …

Automation To Autonomy: Temporal Dynamics Of Trust And Visual Attention Allocation Did Not Evolve, Tetsuya Sato, Eric Chancey, Yusuke Yamani

Psychology Faculty Publications

Emerging work environments are expected to implement autonomy that performs various functions without human input. Previous works has shown that trust in automation is negatively correlated with visual attention allocation, indicating that trust is a dynamic construct. Moreover, trust in automation and trust in autonomy appears to evolve in similar ways. However, recent work has demonstrated differences between trust in automation and trust in autonomy within Kaber’s (2018) theoretical framework (Sato et al., 2023b). Yet, it is uncertain whether the development of trust and visual attention allocation differs between automation and autonomy. The present study examined the temporal dynamics of …

A Method For Ultrasound Servo Tracking For Puncture Needle, Shitong Ye, Bo Yang, Hao Quan, Shan Liu, Minyu Tang, Jiawei Tian

Electrical & Computer Engineering Faculty Publications

Computer-aided surgical navigation technology helps and guides doctors to complete the operation smoothly, which simulates the whole surgical environment with computer technology, and then visualizes the whole operation link in three dimensions. At present, common image-guided surgical techniques such as computed tomography (CT) and X-ray imaging (X-ray) will cause radiation damage to the human body during the imaging process. To address this, we propose a novel Extended Kalman filter-based model that tracks the puncture needle-point using an ultrasound probe. To address the limitations of Kalman filtering methods based on position and velocity, our method of Kalman filtering uses the position …

Cochlear Electrode Insertion Training Model, Sarah Powell, Kaelyn E. Kraley, Nathan J. Smith

Williams Honors College, Honors Research Projects

Cochlear implant surgery is a delicate procedure performed by Otolaryngologists (ENTs) to implant an electronic device into the inner ear to provide a sense of sound for people who are profoundly deaf or hard of hearing. The current practices of training involve cadavers and 3D-printed models. Cadavers are commonly used but are expensive, single-use, and do not provide visual and haptic feedback, which are essential for medical students. 3D printed models are less commonly used and are hard to fabricate and not as realistic. If medical students are not properly trained for this delicate procedure, then risks are significantly increased …

Deep Learning-Assisted Diagnostic System: Apices And Odontogenic Sinus Floor Level Analysis In Dental Panoramic Radiographs, Pei Yi Wu, Yuan-Jin Lin, Yu-Jen Chang, Sung-Tsun Wei, Chiung An Chen, Kuo-Chen Li, Wei-Chen Tu, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Odontogenic sinusitis is a type of sinusitis caused by apical lesions of teeth near the maxillary sinus floor. Its clinical symptoms are highly like other types of sinusitis, often leading to misdiagnosis as general sinusitis by dentists in the early stages. This misdiagnosis delays treatment and may be accompanied by toothache. Therefore, using artificial intelligence to assist dentists in accurately diagnosing odontogenic sinusitis is crucial. This study introduces an innovative odontogenic sinusitis image processing technique, which is fused with common contrast limited adaptive histogram equalization, Min-Max normalization, and the RGB mapping method. Moreover, this study combined various deep learning models …