Kidan Tadesse

BioE Ph.D. Proposal Presentation

Time and Date: 11 AM, Thursday November 20, 2025

Location: Klaus Advanced Computing Building, Conference Room 1212

https://gatech.zoom.us/j/93022502298

Meeting ID: 930 2250 2298  

Advisor: Shu Jia, Ph.D. (Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University)

Thesis Committee:

Ahmet F. Coskun, Ph.D. (Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University)

Brooks Lindsey, Ph.D. (Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University)

Farzad Fereidouni, Ph.D. (Department of Pathology and Laboratory Medicine, Emory University School of Medicine)

Brian S. Robinson, M.D., PhD. (Department of Pathology and Laboratory Medicine, Emory University School of Medicine)

Towards end-to-end super-resolution histology

Super-resolution microscopy surpasses the diffraction limit and reveals subcellular structure with nanometer-scale detail. However, its instrumental complexity, computational demands, and high cost have prevented its adoption in routine clinical histopathology, which still relies largely on brightfield imaging. To address these limitations, we have developed two super-resolution systems that integrate directly with standard epifluorescence microscopes. The first enables rapid, extended-depth (EDoF) and large-field imaging, and the second provides high-speed volumetric super-resolution performance. However, to transition these technologies toward practical clinical use, an automated and efficient computational framework is required to streamline acquisition, reconstruction, and interpretation. The goal of this proposal is to translate these super-resolution imaging systems into a scalable and clinically adaptable workflow for cancer tissue imaging and analysis. In Aim 1, we will develop an automated acquisition-integrated, near real time reconstruction pipeline optimized for large area and EDoF super-resolution imaging. Aim 2 focuses on translating these images into practical pathology workflow through virtual histopathological staining and developing semi-automated ROI selection algorithm for targeted local volumetric super-resolution imaging. Aim 3 will demonstrate and validate the end-to-end super-resolution histology imaging platform, including high throughput acquisition, EDoF and extended FOV near-real time reconstruction, localized 3D imaging, and analysis across diverse human cancer tissues to show its generalizability and readiness for clinical use. Successful completion will result in a scalable super-resolution histology system that can be integrated into current histopathology practice, enabling high-resolution extended depth and 3D assessment of cancer tissues at a throughput compatible with real clinical workflows.