DOI
https://doi.org/10.25772/45J6-TZ62
Defense Date
2021
Document Type
Thesis
Degree Name
Master of Science
Department
Radiation Oncology
First Advisor
Lulin Yuan
Second Advisor
Laura Padilla
Abstract
Visualization of liver tumors on simulation CT scans is challenging even with contrast-enhancement, due to the sensitivity of the contrast enhancement to the timing of the CT acquisition. Image registration to magnetic resonance imaging (MRI) can be helpful for delineation, but differences in patient position, liver shape and volume, and the lack of anatomical landmarks between the two image sets makes the task difficult. This study develops a U-Net based neural network for automated liver and tumor segmentation for purposes of radiotherapy treatment planning. Non-contrast simulation based abdominal CT axial scans of 52 patients with primary liver tumors were utilized. Preprocessing steps included HU windowing to isolate livers from the scan and creating masks for liver and tumor using the radiotherapy structure set (RTSTRUCT) DICOM file, and converting the images to a PNG format. The RTSTRUCT file contained the ground truth contours that were manually labelled by the physician for both liver and tumor. The image slices were split into 1400 for training and 600 for validation. Two fully convolutional neural networks with a U-Net architecture were used in this study. The first U-Net segments the livers. The second U-Net segments the tumor from the liver segments produced from the first network. The dice coefficient for liver segmentation was 89.5% and the dice coefficient for liver tumor segmentation was 44.4%. The results showed that the proposed algorithm had good performance in liver segmentation and shows areas for improvement for liver tumor segmentation.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
8-13-2021
Included in
Artificial Intelligence and Robotics Commons, Diagnosis Commons, Health and Medical Physics Commons, Radiation Medicine Commons, Radiology Commons, Therapeutics Commons