Deep learning vs. conventional methods for automatic quantification of total tumor radioactivity in positron projection images of mouse xenograft tumors

Kevin C. Ma; Michael V. Green; Elaine M. Jagoda; Jurgen Seidel; Peter L. Choyke; Baris Turkbey; Stephanie A. Harmon

doi:10.1117/12.2610759

4 April 2022 Deep learning vs. conventional methods for automatic quantification of total tumor radioactivity in positron projection images of mouse xenograft tumors

Kevin C. Ma, Michael V. Green, Elaine M. Jagoda, Jurgen Seidel, Peter L. Choyke, Baris Turkbey, Stephanie A. Harmon

Author Affiliations +

Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 120361A (2022) https://doi.org/10.1117/12.2610759
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Positron projection imaging (PPI) of tumor-bearing mice under certain circumstances can provide accurate in vivo estimates of total tumor radioactivity, an important pharmacokinetic measurement. However, the number of images generated in these studies is typically very large and many 2D tumor regions-of-interest (ROIs) must be manually defined to obtain accurate radioactivity estimates. In this study, we compared several methods that might allow automatic quantification of tumor radioactivity content. In total, 120 images (n = 81 mice) were acquired in pairs during two separate experiments. The first experimental batch was used for development, and the second as an independent testing cohort. Four methodologies were evaluated, including deep-learning (U-net), region-growing (Level-Set), and thresholding (Otsu, mean value). For all methodologies, preprocessing of the images included uptake normalization to fixed window. Tumor radioactivity is defined as total uptake within a tumor region minus a background estimate. Performance metrics were evaluated for both segmentation results (Sorenson-Dice Coefficient) and radioactivity calculation results (Bland-Altman). Using the test batch data, DICE score for U-net segmentation was 0.82, vs. 0.5-0.6 for the other three methods. Bland-Altman plots showed a mean difference of -0.26 for U-net based calculations vs. -0.5 to -0.8 for the other methods. The U-net approach had the highest accuracy in both segmentation and subsequent radioactivity calculation.

Conference Presentation

Citation Download Citation

Kevin C. Ma, Michael V. Green, Elaine M. Jagoda, Jurgen Seidel, Peter L. Choyke, Baris Turkbey, and Stephanie A. Harmon "Deep learning vs. conventional methods for automatic quantification of total tumor radioactivity in positron projection images of mouse xenograft tumors", Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 120361A (4 April 2022); https://doi.org/10.1117/12.2610759

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