Age encoded adversarial learning for pediatric CT segmentation
Organ segmentation from CT images is critical in early diagnoses of diseases, progress monitoring, pre-operative planning, radiation therapy planning and CT dose estimation. However, data limitation remains one of the main challenges in the medical image segmentation domain. This challenge is particularly huge in pediatric medical imaging due to the patients' heightened sensitivity to radiation. 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 CFG-SegNet (conditional feature generation segmentation network) 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. CFG-SegNet achieves an average segmentation accuracy of 0.681 DSC (Dice Similarity Coefficient) on the prostate, 0.619 DSC on the uterus, 0.912 DSC on the liver, and 0.832 DSC on the heart with 4-fold cross-validation. We compare 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 prostate, uterus, liver, and heart respectively. The results indicate that our high-performing segmentation framework can precisely segment organs when limited training images are available.
National Institute of Health (NIH) Grants U01EB023822
Email Address of Submitting Authorsaba.email@example.com
Submitting Author's InstitutionMarquette University
Submitting Author's Country
- United States of America