Age Encoded Adversarial Learning for Pediatric CT Segmentation

Authors

• Saba Heidari Gheshlaghi, Marquette University
• Chi Nok Enoch Kan, Marquette University
• Taly Gilat Schmidt, Marquette UniversityFollow
• Dong Hye Ye, Marquette University

Document Type

Article

Publication Date

3-2024

Publisher

MDPI

Source Publication

Bioengineering

Source ISSN

2306-5354

Original Item ID

DOI: 10.3390/bioengineering11040319

Abstract

Organ segmentation from CT images is critical in the early diagnosis of diseases, progress monitoring, pre-operative planning, radiation therapy planning, and CT dose estimation. However, data limitation remains one of the main challenges in medical image segmentation tasks. This challenge is particularly huge in pediatric CT segmentation due to children’s heightened sensitivity to radiation. In order to address this issue, we propose a novel segmentation framework with a built-in auxiliary classifier generative adversarial network (ACGAN) that conditions age, simultaneously generating additional features during training. The proposed conditional feature generation segmentation network (CFG-SegNet) was trained on a single loss function and used 2.5D segmentation batches. Our experiment was performed on a dataset with 359 subjects (180 male and 179 female) aged from 5 days to 16 years and a mean age of 7 years. CFG-SegNet achieved an average segmentation accuracy of 0.681 dice similarity coefficient (DSC) on the prostate, 0.619 DSC on the uterus, 0.912 DSC on the liver, and 0.832 DSC on the heart with four-fold cross-validation. We compared the segmentation accuracy of our proposed method with previously published U-Net results, and our network improved the segmentation accuracy by 2.7%, 2.6%, 2.8%, and 3.4% for the prostate, uterus, liver, and heart, respectively. The results indicate that our high-performing segmentation framework can more precisely segment organs when limited training images are available.

Comments

Bioengineering, Vol. 11, No. 4 (March 2024). DOI.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Gheshlaghi, Saba Heidari; Kan, Chi Nok Enoch; Schmidt, Taly Gilat; and Ye, Dong Hye, "Age Encoded Adversarial Learning for Pediatric CT Segmentation" (2024). Biomedical Engineering Faculty Research and Publications. 769.
https://epublications.marquette.edu/bioengin_fac/769