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Biomedical Engineering and Bioengineering Commons™
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Full-Text Articles in Biomedical Engineering and Bioengineering
Breast Density Classification Using Deep Learning, Conrad Thomas Testagrose
Breast Density Classification Using Deep Learning, Conrad Thomas Testagrose
UNF Graduate Theses and Dissertations
Breast density screenings are an accepted means to determine a patient's predisposed risk of breast cancer development. Although the direct correlation is not fully understood, breast cancer risk increases with higher levels of mammographic breast density. Radiologists visually assess a patient's breast density using mammogram images and assign a density score based on four breast density categories outlined by the Breast Imaging and Reporting Data Systems (BI-RADS). There have been efforts to develop automated tools that assist radiologists with increasing workloads and to help reduce the intra- and inter-rater variability between radiologists. In this thesis, I explored two deep-learning-based approaches …
Development Of Deep Learning Neural Network For Ecological And Medical Images, Shaobo Liu
Development Of Deep Learning Neural Network For Ecological And Medical Images, Shaobo Liu
Dissertations
Deep learning in computer vision and image processing has attracted attentions from various fields including ecology and medical image. Ecologists are interested in finding an effective model structure to classify different species. Tradition deep learning model use a convolutional neural network, such as LeNet, AlexNet, VGG models, residual neural network, and inception models, are first used on classifying bee wing and butterfly datasets. However, insufficient data sample and unbalanced samples in each class have caused a poor accuracy. To make improvement the test accuracy, data augmentation and transfer learning are applied. Recently developed deep learning framework based on mathematical morphology …
Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing, Eric Daniel Morris
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 …
Multi-Surface Simplex Spine Segmentation For Spine Surgery Simulation And Planning, Rabia Haq
Multi-Surface Simplex Spine Segmentation For Spine Surgery Simulation And Planning, Rabia Haq
Computational Modeling & Simulation Engineering Theses & Dissertations
This research proposes to develop a knowledge-based multi-surface simplex deformable model for segmentation of healthy as well as pathological lumbar spine data. It aims to provide a more accurate and robust segmentation scheme for identification of intervertebral disc pathologies to assist with spine surgery planning. A robust technique that combines multi-surface and shape statistics-aware variants of the deformable simplex model is presented. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user-assistance is …