Dr. Seung Seok Han Profile

Dr. Seung Seok Han

at Seoul National Univ Hospital

SPIE Involvement:

Author

Publications (2)

Proceedings Article | 3 April 2024 Poster + Paper

Correlation of glomerular histomorphometry changes with spatially resolved transcriptomic profiles in diabetic nephropathy

Ahmed Naglah, Sayat Mimar, Anindya Paul, Ricardo Melo Ferreira, Avi Rosenberg, Seung Seok Han, Jessica Ray, Michael Eadon, Pinaki Sarder

Proceedings Volume 12933, 129331G (2024) https://doi.org/10.1117/12.3008461

KEYWORDS: Tissues, Machine learning, Visualization, Medicine, Diseases and disorders, Visual process modeling, Kidney, Education and training, Data modeling, Statistical modeling, Artificial intelligence

Read Abstract +

Diabetic nephropathy (DN), a common complication of diabetes mellitus, remains a leading cause of endstage renal disease. Histopathological assessment of renal biopsy remains the gold standard for diagnosis. Accurate diagnosis is crucial for timely intervention and personalized management plans. Machine learning (ML) models can analyze digital pathology slides, learn DN biomarkers, and aid in DN staging. Developing ML models can be challenging for the limited availability of annotated images, subjectivity in histopathology interpretation, and histology artifacts. Molecular profiling such as single-cell RNA sequencing (SC) and spatial transcriptomics (ST) can contribute to better understanding of cellular heterogeneity and molecular pathways. Clinical use of molecular tests is limited due to the absence of well-established protocols specific to DN diagnosis. In this study, we propose a framework for correlating glomerular histomorphometry with spatially resolved transcriptomics to better understand the histologic spectrum of DN. The framework uses manual tissue labels by experienced users, and hybrid labels by combining user input and unsupervised clustering of molecular data. Clustering is performed on the gene expression levels of disease biomarkers and on the cell type decomposition of tissue by integration with SC reference data from KPMP. We used a dataset of 6 DN and 3 normal cases, with frozen section histology, ST, and SC collected at Seoul National University Hospital, Seoul, South Korea. Our initial experiments identified a correlation between the imaging features histomorphometry and disease label. Our cloud-based prototype visualizes both gene markers and cell type decomposition as a heatmap on histology, enables molecular-informed validation of structures, enables adding manual labels, and visualizes the clusters on histology. In conclusion, our framework can analyze the correlation between histomorphometry and tissue labels generated in a molecular-informed environment. Our cloud-based prototype can aid the diagnosis process by visualizing these correlations overlaid on digital slides.

Proceedings Article | 7 April 2023 Presentation + Paper

Spatially aware transformer networks for contextual prediction of diabetic nephropathy progression from whole slide images

Benjamin Shickel, Nicholas Lucarelli, Adish Rao, Donghwan Yun, Kyung Chul Moon, Han Seung Seok, Pinaki Sarder

Proceedings Volume 12471, 124710K (2023) https://doi.org/10.1117/12.2655266

KEYWORDS: Transformers, Machine learning, Feature extraction, Education and training, Image segmentation, Pathology, Biopsy, Cross validation, Kidney, Diseases and disorders, Artificial intelligence

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years