Shirong Xue

Graduation Semester and Year




Document Type


Degree Name

Master of Science in Computer Science


Computer Science and Engineering

First Advisor

Junzhou Huang


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.


Whole-slide image, ROI, Gigapixel, Annotation, Mouse tracking, Face tracking


Computer Sciences | Physical Sciences and Mathematics


Degree granted by The University of Texas at Arlington