(Tuesday, 7/16/2019) 1:45 PM - 3:45 PM
Room: Stars at Night Ballroom 2-3
Purpose: Accurate and timely organs-at-risk (OAR) 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 OARs on chest CTs 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 (FCNs) as discriminators. The generator, composed of U-Net, produces image segmentation map of multiple organs by an end-to-end mapping learned from CT image to multi-organ segmented OARs. The discriminator, structured as an FCN, discriminates between the ground truth and segmented OARs 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 OARs (ground truth) for quantitative evaluations in geometric difference.
Results: This segmentation technique was applied to delineate the left and right lungs, spinal cord, esophagus, and heart using 35 patients’ chest CTs. The averaged dice similarity coefficient for the above five OARs are 0.97, 0.97, 0.90, 0.75 and 0.87, respectively. The mean surface distance of the five OARs obtained with proposed method ranges between 0.4 mm and 1.5 mm on average among all 35 patients.
Conclusion: We have investigated a novel deep-learning-based approach with a GAN strategy to segment multiple OARs 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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