Kakadiaris, Ioannis A.2020-06-09August 2022020-08August 202Portions of this document appear in: Memariani, Ali, Bradley T. Endres, Eugénie Bassères, Kevin W. Garey, and Ioannis A. Kakadiaris. "RISEC: Rotational Invariant Segmentation of Elongated Cells in SEM Images with Inhomogeneous Illumination." In International Symposium on Visual Computing, pp. 553-563. Springer, Cham, 2019. And in: Memariani, Ali, and Ioannis A. Kakadiaris. "SoLiD: segmentation of clostridioides difficile cells in the presence of inhomogeneous illumination using a deep adversarial network." In International Workshop on Machine Learning in Medical Imaging, pp. 285-293. Springer, Cham, 2018. And in: Memariani, Ali, Christophoros Nikou, B. T. Endres, E. Basseres, K. W. Garey, and Ioannis A. Kakadiaris. "DETCIC: Detection of Elongated Touching Cells with Inhomogeneous Illumination using a Stack of Conditional Random Fields." In VISIGRAPP (4: VISAPP), pp. 574-580. 2018. And in: Memariani, Ali, Christophoros Nikou, B. T. Endres, E. Basseres, K. W. Garey, and Ioannis A. Kakadiaris. "DeTEC: detection of touching elongated cells in sem images." In International Symposium on Visual Computing, pp. 288-297. Springer, Cham, 2016.https://hdl.handle.net/10657/6744Clostridioides difficile infection (CDI) is a significant cause of death and morbidity due to infec- tious gastroenteritis in the USA. Treatments for CDI are being developed and comparison of the treatments is of paramount importance. Conventional microbiology methods investigate the effec- tiveness 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 illumi- nation, which causes shadows on the cells and bright areas around the cells, and (2) the 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 bound- ing 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. Finally, the performance is evaluated and compared to the state-of-the-art in instant-level object segmentation. The results indicate that the proposed computational tool out-performs the state-of-the-art method Mask-RCNN in detection (mean average precision) and segmentation (dice score) of CDI cells in SEM images.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).Clostridioides difficile infection (C. diff)Cell detectionCell segmentationDeep adversarial networkIllumination2020-06-09Thesisborn digital