Effects and methodology for grid subdivision of CT-based texture for unsupervised clustering

Kenneth G. M. Cunanan; Bino Varghese; Yenlin Lee; Raghda Abouelnaga; Ramin Eghtesadi; Darryl Hwang; Vinay Duddalwar; Steven Cen

doi:10.1117/12.2670140

6 March 2023 Effects and methodology for grid subdivision of CT-based texture for unsupervised clustering

Kenneth G. M. Cunanan, Bino Varghese, Yenlin Lee, Raghda Abouelnaga, Ramin Eghtesadi, Darryl Hwang, Vinay Duddalwar, Steven Cen

Author Affiliations +

Proceedings Volume 12567, 18th International Symposium on Medical Information Processing and Analysis; 125670F (2023) https://doi.org/10.1117/12.2670140
Event: 18th International Symposium on Medical Information Processing and Analysis, 2022, Valparaíso, Chile

Abstract

t-Distributed Stochastic Neighbor Embedding (t-SNE) and k-means have been increasingly utilized for dimension reduction and graphical illustration in medical imaging (e.g., CT) informatics. Mapping a grid network onto a slide is a prerequisite for implementing cluster analysis. Traditionally, the performance of cluster analysis is driven by hyperparameters, however, grid size which also affects performance is often set arbitrarily. In this study, we evaluated the effect of varying grid sizes, perplexity and learning rate hyperparameters for unsupervised clustering using CT images of renal masses. We investigated the impact of grid size to cluster analysis. The number of clusters was determined by Gap-statistics. The grid size selections were 2x2, 4x4, 5x5, and 8x8. The results showed that the number of output clusters increased with decreasing grid sizes from 8x8 to 4x4. However, when grid size reached 2x2, the model yielded the same cluster number as 8x8. This finding was consistent across different hyperparameter settings. Additional analyses were conducted to understand the nesting structure between the cluster membership (the mutually exclusive cluster number assigned to each grid in a cluster analysis) from large (8x8) grid and small (2x2) grid, although both grid size selections yielded the same number of clusters. We report that the cluster membership between large grid and small grid is only partially overlaid. This suggests that additional pattern/information is detected by using the small grid. In conclusion, the grid size should be treated as another hyperparameter when using unsupervised clustering methods for pattern recognition in medical imaging analysis.

Citation Download Citation

Kenneth G. M. Cunanan, Bino Varghese, Yenlin Lee, Raghda Abouelnaga, Ramin Eghtesadi, Darryl Hwang, Vinay Duddalwar, and Steven Cen "Effects and methodology for grid subdivision of CT-based texture for unsupervised clustering", Proc. SPIE 12567, 18th International Symposium on Medical Information Processing and Analysis, 125670F (6 March 2023); https://doi.org/10.1117/12.2670140

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Machine learning

Tumors

Computed tomography

Visualization

Image processing

Image segmentation

Medical imaging

Show All Keywords

Keywords/Phrases

Search In:

Publication Years