Graduation Semester and Year
2017
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
Junzhou Huang
Abstract
Digital Pathology is a very promising approach to diagnostic medicine to accomplish better, faster prognosis and prediction of cancer. The high-resolution whole slide imaging (WSI) can be analyzed on any computer, easily stored, and quickly shared. However, a digital WSI is quite large, like over 1M pixels by 1M pixels (3TB), depending on the tissue and the biopsy type. Automatic localization of regions of interest (ROIs) is important because it decreases the computational load and improves the diagnostic accuracy. Some popular applications in the market already support in viewing and marking the ROIs, such as ImageScope, OpenSlide, and ImageJ. However, it only shows some regions as a result and is hard to learn pathologists' behavior for future research and education. In this thesis, we propose a new automatic system, named as Auto-ROI, to automatically localize and extract diagnostically relevant ROIs from the pathologists' daily actions when they are viewing the WSI. Analyzing action information enables researchers to study pathologists' interpretive behavior and gain a new understanding of the diagnostic medical decision-making process. We compare the ROIs extracted by the proposed system with the ROIs marked by ImageScope in order to evaluate the accuracy. Experiment results show the Auto-ROI System can help to achieve a good performance in survival analysis.
Keywords
Whole-slide image, ROI, Gigapixel, Annotation, Mouse tracking, Face tracking
Disciplines
Computer Sciences | Physical Sciences and Mathematics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Xue, Shirong, "AUTO-ROI SYSTEM: AUTOMATIC LOCALIZATION OF ROI IN GIGAPIXEL WHOLE-SLIDE IMAGES" (2017). Computer Science and Engineering Theses. 484.
https://mavmatrix.uta.edu/cse_theses/484
Comments
Degree granted by The University of Texas at Arlington