Angela Deng
Pronouns: She/Her/Hers
Research Mentor(s): Reza Soroushmehr, Research Investigator
Research Mentor School/College/Department: Department of Computational Medicine & Bioinformatics, Michigan Medicine
Presentation Date: Thursday, April 22, 2021
Session: Session 1 (10am-10:50am)
Breakout Room: Room 16
Presenter: 4
Abstract
Cerebral edema, which is swelling in the brain, is commonly found in patients suffering from head trauma, injuries, or other diseases and can be fatal. The purpose of this research project was to develop a method for automatically detecting and segmenting edema in head CT scans in order to make it faster and easier for clinicians to diagnose and treat traumatic brain injury (TBI) patients. However, edema is difficult to segment due to its unclear boundaries and its similarity in pixel value to other brain tissue. In previous research, most methods for segmenting edema have either been semi-automated or for MRI scans. More accurate methods require MRI scans, but even though an MRI scan is more detailed and can make it easier to segment edema, CT scan is the gold standard for evaluating brain injuries and is faster and more widely available. Therefore, automatic segmentation of CT scans will be very beneficial. In this project, the active contours without edges method, developed by Chan and Vese, is used with manually segmented hematoma as the initial contour. The method was developed in MATLAB. The segmented edema was then compared with the manually segmented images, and the DICE score was used to measure the accuracy. Currently, this method successfully segments select CT scans. However, it needs improvement in order to be more generalizable. In the future, I will look further into other techniques such as deep learning that can help improve accuracy and generalizability in automatic edema segmentation.
Authors: Angela Deng, Reza Soroushmehr
Research Method: Computer Programming
