Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN

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 OARs on chest computed tomography (CT) for radiotherapy treat...

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Vydáno v:	Medical physics (Lancaster) Ročník 46; číslo 5; s. 2157 - 2168
Hlavní autoři:	Dong, Xue, Lei, Yang, Wang, Tonghe, Thomas, Matthew, Tang, Leonardo, Curran, Walter J., Liu, Tian, Yang, Xiaofeng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States 01.05.2019
Témata:	chest segmentation deep learning Esophagus - diagnostic imaging Esophagus - radiation effects Heart - diagnostic imaging Heart - radiation effects Humans Image Processing, Computer-Assisted - methods Lung - diagnostic imaging Lung - radiation effects Neoplasms - radiotherapy Organs at Risk - radiation effects Radiography, Thoracic - methods Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted - methods Radiotherapy, Intensity-Modulated - methods Spinal Cord - diagnostic imaging Spinal Cord - radiation effects Tomography, X-Ray Computed - methods chest segmentation CT deep learning
ISSN:	0094-2405, 2473-4209, 2473-4209
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Shrnutí:	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 OARs 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 (FCNs) 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 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, 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 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 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 OARs. 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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0094-2405 2473-4209 2473-4209
DOI:	10.1002/mp.13458