In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
will defend his dissertation
Analysis of Scanning Electron Microscopy Images with Illumination Inhomogeneity and Touching-Crossing Cells
AbstractClostridioides difficile infection (CDI) is a significant cause of death and morbidity due to infectious gastroenteritis in the USA. Treatments for CDI are being developed and comparison of the treatments is of paramount importance. Conventional microbiology methods investigate the effectiveness of treatments on the macro-level, and a phenotypic investigation has not been performed. Phenotypic features (e.g., length, shape deformation) of CDI cells in scanning electron microscopy (SEM) images indicate critical information about cell health in CDI research studies. However, analysis of SEM images is challenging due to the following challenges: (1) inhomogeneous illumination, which causes shadows on the cells and bright areas around the cells, and (2) presence of touching and crossing cells. Therefore, there is an urgent critical need to develop methods for the segmentation of the CDI cells to extract phenotypic information. This work presents a deep learning pipeline to provide instant-level segmentation of CDI cells in scanning electron microscopy images. The components are: (i) an adversarial region proposal network to compute cell candidate bounding boxes, and (ii) an instance-level segmentation network extracting features from bounding boxes, and computing the segmentation masks of isolated, touching, and crossing cells. The pipeline provides a computational tool for analysis of scanning electron microscopy images which is critical to compare the efficacy of CDI treatments.
Date: Wednesday, November 20, 2019
Time: 9:30 - 11:00 AM
Place: HBS 317
Advisor: Dr. Ioannis Kakadiaris
Faculty, students, and the general public are invited.