Automatic multiorgan segmentation in thorax CT images using U‐net‐ GAN
Open accessAbstract
Purpose: Accurate and timely organs‐at‐risk ( OARs ) segmentation is key to efficient and high‐quality radiation therapy planning. The purpose of this work is to develop a deep learning‐based method to automatically segment multiple thoracic OAR s on chest computed tomography ( CT ) for radiotherapy treatment planning. Methods: We propose an adversarial training strategy to train deep neural networks for the segmentation of multiple organs on thoracic CT images. The proposed design of adversarial networks, called U‐Net‐generative adversarial network (U‐Net‐ GAN ), jointly trains a set of U‐Nets as generators and fully convolutional networks ( FCN s) as discriminators. Specifically, the generator, composed of U‐Net, produces an image segmentation map of multiple organs by an end‐to‐end mapping learned from CT image to multiorgan‐segmented OAR s. The discriminator, structured as an FCN , discriminates between the ground truth and segmented OAR s produced by the generator. The generator and discriminator compete against each other in an adversarial learning process to produce the optimal segmentation map of multiple organs. Our segmentation results were compared with manually segmented OAR s (ground truth) for quantitative evaluations in geometric difference, as well as dosimetric performance by investigating the dose‐volume histogram in 20 stereotactic body radiation therapy ( SBRT ) lung plans. Results: This segmentation technique was applied to delineate the left and right lungs, spinal cord, esophagus, and heart using 35 patients’ chest CT s. The averaged dice similarity coefficient for the above five OAR s are 0.97, 0.97, 0.90, 0.75, and 0.87, respectively. The mean surface distance of the five OAR s obtained with proposed method ranges between 0.4 and 1.5 mm on average among all 35 patients. The mean dose differences on the 20 SBRT lung plans are −0.001 to 0.155 Gy for the five OAR s. Conclusion: We have investigated a novel deep learning‐based approach with a GAN strategy to segment multiple OAR s in the thorax using chest CT images and demonstrated its feasibility and reliability. This is a potentially valuable method for improving the efficiency of chest radiotherapy treatment planning.
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The IoU values in Table 1 are consistent with published benchmarks for U-Net-based thoracic OAR segmentation.
“The IoU values in Table 1 are consistent with published benchmarks for U‐Net‐based thoracic OAR segmentation. 26”
From Uncertainty quantification of U‐Net based segmentation tool using conformal prediction (2026) · cites this paper as [26]
The Results (page not supplied) provide published performance figures for U-Net-GAN segmentation of thoracic organs at risk, and the Discussion compares its performance with other methods. However, the paper reports Dice scores and other metrics, not IoU, so it does not directly establish that the citing paper’s Table 1 IoU values are consistent with its results. (LLM, conf=0.84)
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- Authors
- Dong, X., Lei, Y., Wang, T., Thomas, M., Tang, L., Curran, W. J., Liu, T., & Yang, X.
- Affiliations
- Emory University (US)
- University of California, Berkeley (US)
- Corresponding author
- Yang, Xiaofeng
- Venue
- Medical Physics
- Publisher
- Wiley
- Published
- 2019-03-22
- Volume
- 46
- Issue
- 5
- Pages
- 2157-2168
- ISSN
- 0094-2405 (ISSN-L)
- 2473-4209
- PMC
- PMC6510589
- OpenAlex
- W2916412824
- Cited by
- 275
- References
- 38
- Citations by year
- 2026: 12 · 2025: 24 · 2024: 36 · 2023: 34 · 2022: 45 · 2021: 75 · 2020: 40 · 2019: 9
- FWCI
- 18.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Medical Physics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (R01CA215718)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- organ-at-risk segmentation, chest CT, generative adversarial networks, radiotherapy treatment planning, stereotactic body radiotherapy, lung radiotherapy, spinal cord, esophagus, heart, lung segmentation, dose-volume histograms
- MeSH terms
- Esophagus, Heart, Humans, Image Processing, Computer-Assisted, Lung, Neoplasms, Radiotherapy Dosage, Radiotherapy Planning, Computer-Assisted, Spinal Cord, Radiography, Thoracic, Tomography, X-Ray Computed, Radiotherapy, Intensity-Modulated, Organs at Risk
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