Bioimaging and Biomedical Optics Commons^™

Deep Learning-Based Breast Cancer Detection And Classification Using Histopathology Images, Ghulam Murtaza

Student Works (2020-2029)

Cancer disease is drastically increasing worldwide over the past few years. Among all types of cancers in women, breast cancer (BrC) is the main cause of abnormal deaths. For a confident diagnosis of BrC, histopathology (Hp) images are usually suggested by the doctors. BrC detection is a diagnostic test for benign (non-cancerous) and malignant (cancerous) breast tumors (BrT). Once the BrT is diagnosed, then it needs to be classified for subtypes of benign and malignant to start specific treatment. Several studies developed BrC detection and classification models using Hp images. However, the existing models required high computational resources, long training …

Computational Modelling Enables Robust Multidimensional Nanoscopy, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

The following sections are included:

• Present State of Computational Modelling in Fluorescence Nanoscopy

• Recent Contributions to Computational Modelling in Fluorescence Nanoscopy

• Outlook on Computational Modelling in Fluorescence Nanoscopy

• Acknowledgments

• References

Focused Ultrasound-Mediated Drug Delivery To The Brainstem, Dezhuang Ye

McKelvey School of Engineering Graduate Student Theses & Dissertations

Brainstem gliomas are tumors that occur in the brainstem, the brain region that connects the brain to the spinal cord and controls vital body functions. The critical anatomic location of the brainstem precludes surgical intervention and limits the use of invasive therapeutic techniques. Moreover, the frequently intact blood-brain barrier (BBB) of most brainstem gliomas prevents therapeutic agents from reaching the diseased site. The currently available techniques for brain drug delivery are either invasive (e.g., convection-enhanced delivery) or lack targeting to the diseased site (e.g., intranasal brain drug delivery). Novel techniques that can noninvasively overcome the BBB are critically needed for …

Three-Dimensional (3d) Reconstruction Of Computer Tomography Cardiac Images Using Visualization Toolkit (Vtk), Mohammad Zahid Zamaludin

Student Works (2020-2029)

Cardiac study proves to be important to diagnose and treat cardiovascular disease that accounts for 20% of death in the world. Three-dimensional (3D) images of the heart are important to understand its real structure, functions and physical abnormalities occurring in the tissue. The objective of this paper is to reconstruct 3D images from 2D CT Scan slices of the cardiac region of interest using Visualisation Toolkit (VTK). The Visualization Toolkit (VTK) is non-propriety software with state-of-the-art tools for 3D rendering that is easily customizable programming. 3D images are reconstructed using surface rendering technique and volume rendering technique. Volume rendering shows …

Characterization & Calibration Of Foresight Ice, Hareem Nisar, Terry M Peters, Elvis C.S. Chen

Robarts Imaging Publications

No abstract provided.

Imaging Potential In Saturation Recovery Methods For Sarcoidosis Patients With Medical Devices, Samantha Zhao

Theses and Dissertations

Cardiovascular magnetic resonance (CMR) imaging is a preferred imaging methodology due to its lack of ionizing radiation and ability to detect myocardial inflammation and fibrosis using quantitative T1 mapping techniques. Cardiac sarcoidosis (CS) is characterized as the formation of granulomas in the myocardium. Current methods for detection include measuring non-cardiac specific C-reactive protein (CRP) levels, or PET imaging, which uses ionizing radiation, therefore CMR would make an ideal imaging option. However, many CS patients have implanted cardiac devices which can cause degradation in image. The modified Look-Locker inversion recovery (MOLLI) method is widely used in quantitative T1 mapping with high …

Assessing Midbrain Abnormalities In Parkinson’S Disease Using Magnetic Resonance Imaging, Kiarash Ghassaban

Wayne State University Dissertations

Diagnosing early-stage Parkinson’s disease (PD) and its manifestations is still a clinical challenge. Previous imaging studies using iron, neuromelanin (NM) and the Nigrosome-1 (N1) measures in the substantia nigra (SN) by themselves have been unable to provide sufficiently high diagnostic performance for these methods to be adopted clinically. In this dissertation, we start by studying idiopathic PD patients at their intermediate stages of the disease to evaluate the role of global and regional iron in the major deep gray matter nuclei. Then, we only focus on the NM complex in the midbrain and how neuronal loss interact with iron overload …

Perceptually Improved Medical Image Translations Using Conditional Generative Adversarial Networks, Anurag Vaidya

Honors Theses

Magnetic resonance imaging (MRI) can help visualize various brain regions. Typical MRI sequences consist of T1-weighted sequence (favorable for observing large brain structures), T2-weighted sequence (useful for pathology), and T2-FLAIR scan (useful for pathology with suppression of signal from water). While these different scans provide complementary information, acquiring them leads to acquisition times of ~1 hour and an average cost of $2,600, presenting significant barriers. To reduce these costs associated with brain MRIs, we present pTransGAN, a generative adversarial network capable of translating both healthy and unhealthy T1 scans into T2 scans. We show that the addition of non-adversarial …

Development Of Light Actuated Chemical Delivery Platform On A 2-D Array Of Micropore Structure, Hojjat Rostami Azmand, Hojjat Rostami Azmand

Dissertations and Theses

Localized chemical delivery plays an essential role in the fundamental information transfers within biological systems. Thus, the ability to mimic the natural chemical signal modulation would provide significant contributions to understand the functional signaling pathway of biological cells and develop new prosthetic devices for neurological disorders. In this paper, we demonstrate a light-controlled hydrogel platform that can be used for localized chemical delivery in a high spatial resolution. By utilizing the photothermal behavior of graphene-hydrogel composites confined within micron-sized fluidic channels, patterned light illumination creates the parallel and independent actuation of chemical release in a group of fluidic ports. The …

When The Brain Plays A Game: Neural Responses To Visual Dynamics During Naturalistic Visual Tasks, Jason Ki

Dissertations and Theses

Many day-to-day tasks involve processing of complex visual information in a continuous stream. While much of our knowledge on visual processing has been established from reductionist approaches in lab-controlled settings, very little is known about the processing of complex dynamic stimuli experienced in everyday scenarios. Traditional investigations employ event-related paradigms that involve presentation of simple stimuli at select locations in visual space and discrete moments in time. In contrast, visual stimuli in real-life are highly dynamic, spatially-heterogeneous, and semantically rich. Moreover, traditional experiments impose unnatural task constraints (e.g., inhibited saccades), thus, it is unclear whether theories developed under the reductionist …

Clinical Applications And Feasibility Of Proton Ct And Proton Radiography, Christina Marie Sarosiek

Graduate Research Theses & Dissertations

Proton therapy is a form of radiation treatment for cancer that utilizes the Bragg peak to create conformal high dose regions around the tumor volume. However, the use of x-ray computed tomography (CT) and x-ray radiography for treatment planning and pre-treatment quality assurance procedures improves the achievable effectiveness of proton treatment plans (using proton CT) and the pretreatment verification (using proton radiography). Errors in the conversion from x-ray Hounsfield units (HU) to proton relative stopping powers (RSP) leads to errors in the predicted proton range. To account for the errors, 3.5% margins are included in the treatment plan. This means …

Fmri Feature Extraction Model For Adhd Classification Using Convolutional Neural Network, Senuri De Silva, Sanuwani Udara Dayarathna, Gangani Ariyarathne, Dulani Meedeniya, Sampath Jayarathna

Computer Science Faculty Publications

Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With the advancements of computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder, with the symptomology of inattention, impulsivity, and hyperactivity, in which early diagnosis is crucial to prevent unwelcome outcomes. This study addresses ADHD identification using functional magnetic resonance imaging (fMRI) data for the resting state brain by evaluating multiple feature extraction methods. The features of seed-based correlation (SBC), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) are comparatively applied to …

Combining Cryo-Em Density Map And Residue Contact For Protein Secondary Structure Topologies, Maytha Alshammari, Jing He

Computer Science Faculty Publications

Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set …

Bibliometric Review On Applications Of Disease Detection Using Digital Image Processing Techniques, Jayant Jagtap, Rahil Sharma, Aryan Sinha, Nikhil Panda, Amulya Reddy

Library Philosophy and Practice (e-journal)

Advances around the field of deep learning and cognitive computing have allowed mankind to look and solve the problems of the world in a completely new way. Deep learning has been making huge advancements in the field of healthcare, which most importantly focuses upon disease detection and disease prediction. Techniques such as these have been conceptualized the idea of early detection and economical ways of treating the predicted disease in particular. Still, it has been observed that there seems to be no change in the way diagnosis of a particular disease takes place even in the 21^st generation of …

Bibliometric Review On Liver And Tumour Segmentation Using Deep Learning, Jayant Jagtap, Aamir Habeeb, Avinash Jha, Shrey Aggarwal, Khushi Gupta

Library Philosophy and Practice (e-journal)

One of the major organs in the body is liver where tumors occur often. Malignant liver tumors pose a serious hazard to human life and health. Manual segmentation of the liver organ and tumor from computed tomography (CT) scans is difficult, time-consuming, and skewed to the clinician's experience, yet it is essential for hepatic surgical planning. However, due to the following considerations, segmenting liver tumors from computed tomography (CT) images is difficult: In CT pictures, the contrast between the liver tumor and healthy tissues is low, and the boundary is indistinct; the picture of the liver tumor is confusing, with …

Nanoscale Material Interaction With Multicellular 3d Cancer Culture, Emilie Darrigues

Theses and Dissertations

Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, it is difficult to diagnose, often recurs, has high resistance to chemotherapies, and can be marked by advanced metastatic stages. There is an urgent need to fill the knowledge on nanoparticle penetration, diffusion, and cellular interaction in the context of adaptive PDAC tumor microenvironment to allow the development of new imaging and therapeutic supports. Traditionally, cancer-treating nanoparticles have been engineered and tested on 2D cell cultures, but several studies indicate that this in vitro model does not mimic tumor features closely enough. In contrast, 3D culture models offer …

Imparting 3d Representations To Artificial Intelligence For A Full Assessment Of Pressure Injuries., Sofia Zahia

Electronic Theses and Dissertations

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning …

Network-Level Mechanisms Underlying Effects Of Transcranial Direct Current Stimulation (Tdcs) On Visuomotor Learning, Pejman Sehatpour, Clément Dondé, Matthew J. Hoptman, Johanna Kreither, Devin Adair, Elisa Dias, Blair Vail, Stephanie Rohrig, Gail Silipo, Javier Lopez-Calderon, Antigona Martinez, Daniel C. Javitt

Publications and Research

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation approach in which low level currents are administered over the scalp to influence underlying brain function. Prevailing theories of tDCS focus on modulation of excitation-inhibition balance at the local stimulation location. However, network level effects are reported as well, and appear to depend upon differential underlying mechanisms. Here, we evaluated potential network-level effects of tDCS during the Serial Reaction Time Task (SRTT) using convergent EEG- and fMRI-based connectivity approaches. Motor learning manifested as a significant (p <.0001) shift from slow to fast responses and corresponded to a significant increase in beta-coherence (p <.0001) and fMRI connectivity (p <.01) particularly within the visual-motor pathway. Differential patterns of tDCS effect were observed within different parametric task versions, consistent with network models. Overall, these findings demonstrate objective physiological effects of tDCS at the network level that result in effective behavioral modulation when tDCS parameters are matched to network-level requirements of the underlying task.

Understanding Radiation Resistance In Head And Neck Tumor Xenografts Using Diffuse Reflectance And Raman Spectroscopy, Sina Dadgar

Graduate Theses and Dissertations

Each year, 800,000 new patients are diagnosed with head and neck squamous cell carcinoma (HNSCC), a majority of whom are treated with a combination of daily fractions of radiation and weekly chemotherapy sessions for up to seven weeks. Current methods to evaluate treatment response of individual patients are limited to anatomical measurements of tumor burden using CT scan or MRI 4-8 weeks after completion of treatment. However, earlier knowledge of radiation-response prior to or at early days after commencement of therapy can aid oncologist with escalating and de-escalating treatment plans for exceptionally non-responding and responding patients. Such a knowledge can …

Multiphoton Microscopy And Deep Learning Neural Networks For The Automated Quantification Of In Vivo, Label-Free Optical Biomarkers Of Skin Wound Healing, Jake D. Jones

Graduate Theses and Dissertations

Non-healing ulcerative wounds that occur frequently in diseases such as diabetes are challenging to diagnose and treat due to numerous possible etiologies and the variable efficacy of wound care products. With advanced age, skin wound healing is often delayed, leaving elderly patients at high risk for developing these chronic injuries. As it is challenging to discriminate age-related delays from disease-related chronicity, there is a critical need to develop new quantitative biomarkers that are sensitive to wound status. Multiphoton microscopy (MPM) techniques are well-suited for 3D imaging of epithelia and are capable of non-invasively detecting metabolic cofactors (NADH and FAD) without …