Date of Award
May 2020
Degree Type
Thesis
Degree Name
Master of Science
Department
Engineering
First Advisor
Zeyun Yu
Committee Members
Jun Zhang, Sandeep Gopalakrishnan
Keywords
convolutional neural networks, deep learning, medical image segmentation
Abstract
Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images is time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images have been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and spark research interests in medical image segmentation using deep learning. We propose two convolutional frameworks to segment tissues from different types of medical images. Comprehensive experiments and analyses are conducted on various segmentation neural networks to demonstrate the effectiveness of our methods. Furthermore, datasets built for training our networks and full implementations are published.
Recommended Citation
Wang, Chuanbo, "Medical Image Segmentation with Deep Learning" (2020). Theses and Dissertations. 2434.
https://dc.uwm.edu/etd/2434