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Full-Text Articles in Engineering
Automatic Cardiac Mri Image Segmentation And Mesh Generation, Ziyuan Li
Automatic Cardiac Mri Image Segmentation And Mesh Generation, Ziyuan Li
McKelvey School of Engineering Theses & Dissertations
Segmenting and reconstructing cardiac anatomical structures from magnetic resonance (MR) images is essential for the quantitative measurement and automatic diagnosis of cardiovascular diseases [1]. However, manual evaluation of the time-series cardiac MRI (CMRI) obtained during routine clinical care are laborious, inefficient, and tends to produce biased and non-reproducible results [2]. This thesis proposes an end-to-end pipeline for automatically segmenting short-axis (SAX) CMRI images and generating high-quality 2D and 3D meshes suitable for finite element analysis. The main advantage of our approach is that it can not only work as a stand-alone pipeline for the automatic CMR image segmentation and mesh …
Photoacoustic Imaging Of Colorectal Cancer And Ovarian Cancer, Xiandong Leng
Photoacoustic Imaging Of Colorectal Cancer And Ovarian Cancer, Xiandong Leng
McKelvey School of Engineering Theses & Dissertations
Photoacoustic (PA) imaging is an emerging hybrid imaging technology that uses a short-pulsed laser to excite tissue. The resulting photoacoustic waves are used to image the optical absorption distribution of the tissue, which is directly related to micro-vessel networks and thus to tumor angiogenesis, a key process in tumor growth and metastasis. In this thesis, the acoustic-resolution photoacoustic microscopy (AR-PAM) was first investigated on its role in human colorectal tissue imaging, and the optical-resolution photoacoustic microscopy (OR-PAM) was investigated on its role in human ovarian tissue imaging.Colorectal cancer is the second leading cause of cancer death in the United States. …
Deep Learning For Automatic Microscopy Image Analysis, Shenghua He
Deep Learning For Automatic Microscopy Image Analysis, Shenghua He
McKelvey School of Engineering Theses & Dissertations
Microscopy imaging techniques allow for the creation of detailed images of cells (or nuclei) and have been widely employed for cell studies in biological research and disease diagnosis in clinic practices.Microscopy image analysis (MIA), with tasks of cell detection, cell classification, and cell counting, etc., can assist with the quantitative analysis of cells and provide useful information for a cellular-level understanding of biological activities and pathology. Manual MIA is tedious, time-consuming, prone to subject errors, and are not feasible for the high-throughput cell analysis process. Thus, automatic MIA methods can facilitate all kinds of biological studies and clinical tasks. Conventional …
Deep Learning For Task-Based Image Quality Assessment In Medical Imaging, Weimin Zhou
Deep Learning For Task-Based Image Quality Assessment In Medical Imaging, Weimin Zhou
McKelvey School of Engineering Theses & Dissertations
It has been advocated to use objective measures of image quality (IQ) for assessing and optimizing medical imaging systems. Objective measures of IQ quantify the performance of an observer at a specific diagnostic task. Binary signal detection tasks and joint signal detection and localization (detection-localization) tasks are commonly considered in medical imaging. When optimizing imaging systems for binary signal detection tasks, the performance of the Bayesian Ideal Observer (IO) has been advocated for use as a figure-of-merit (FOM). The IO maximizes the observer performance that is summarized by the receiver operating characteristic (ROC) curve. When signal detection-localization tasks are considered, …
Multi-Gpu Acceleration Of Iterative X-Ray Ct Image Reconstruction, Ayan Mitra
Multi-Gpu Acceleration Of Iterative X-Ray Ct Image Reconstruction, Ayan Mitra
McKelvey School of Engineering Theses & Dissertations
X-ray computed tomography is a widely used medical imaging modality for screening and diagnosing diseases and for image-guided radiation therapy treatment planning. Statistical iterative reconstruction (SIR) algorithms have the potential to significantly reduce image artifacts by minimizing a cost function that models the physics and statistics of the data acquisition process in X-ray CT. SIR algorithms have superior performance compared to traditional analytical reconstructions for a wide range of applications including nonstandard geometries arising from irregular sampling, limited angular range, missing data, and low-dose CT. The main hurdle for the widespread adoption of SIR algorithms in multislice X-ray CT reconstruction …