Abstract
Automatic delineation of organs at risk (OAR) in computed tomography (CT) images is a crucial step for treatment planning in radiation oncology. However, manual delineation of organs is a challenging and time-consuming task subject to inter-observer variabilities. Automatic organ delineation has been relying on non-rigid registrations and atlases. However, lately deep learning appears as a strong competitor with specific architectures dedicated to image segmentation like UNet. In this paper, we first assessed the standard UNet to delineate multiple organs in CT images. Second, we observed the effect of dilated convolutional layers in UNet to better capture the global context from the CT images and effectively learn the anatomy, which results in increased localization of organ delineation. We evaluated the performance of a standard UNet and a dilated UNet (with dilated convolutional layers) on four chest organs (esophagus, left lung, right lung, and spinal cord) from 29 lung image acquisitions and observed that dilated UNet delineates the soft tissues notably esophagus and spinal cord with higher accuracy than the standard UNet. We quantified the segmentation accuracy of both models by computing spatial overlap measures like Dice similarity coefficient, recall & precision, and Hausdorff distance. Compared to the standard UNet, dilated UNet yields the best Dice scores for soft organs whereas for lungs, no significant difference in the Dice score was observed: \(0.84\pm 0.07\) vs \(0.71\pm 0.10\) for esophagus, \(0.99\pm {0.01}\) vs \(0.99\pm {0.01}\) for left lung, \(0.99\pm {0.01}\) vs \(0.99\pm {0.01}\) for right lung and \(0.91\pm {0.05}\) vs \(0.88\pm {0.04}\) for spinal cord.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zhang, T., Chi, Y., Elisa, M., Di, Y.: Automatic delineation of on-line head-and-neck computed tomography images: toward on-line adaptive radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 68(2), 522–530 (2007)
Gorthi, S., et al.: Segmentation of head and neck lymph node regions for radiotherapy planning using active contour-based atlas registration. IEEE J. Sel. Top. Signal Process. 3(1), 135–147 (2009)
Dolz, J., et al.: Interactive contour delineation of organs at risk in radiotherapy: clinical evaluation on NSCLC patients. Med. Phys. 43(5), 2569–2580 (2016)
Wolz, R., Chu, C., Misawa, K., Fujiwara, M., Mori, K., Rueckert, D.: Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE Trans. Med. Imaging 32(9), 1723–1730 (2013)
Okada, T., Linguraru, M.G., Hori, M., Summers, R.M., Tomiyama, N., Sato, Y.: Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors. Med. Image Anal. 26(1), 1–18 (2015)
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Cha, K.H., Hadjiiski, L., Samala, R.K., Chan, H.-P., Caoili, E.M., Cohan, R.H.: Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets. Med. Phys. 43(4), 1882–1896 (2016)
Roth, H.R., et al.: DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 556–564. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_68
Li, X., et al.: H-DenseUNet: hybrid densely connected UNet for liver and liver tumor segmentation from CT volumes. arXiv preprint arXiv:1709.07330 (2017)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)
Gibson, E., et al.: Towards image-guided pancreas and biliary endoscopy: automatic multi-organ segmentation on abdominal CT with dense dilated networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 728–736. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_83
Chen, L.-C., George, P., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)
Di Perri, D., et al.: Evolution of [18f] fluorodeoxyglucose and [18f] fluoroazomycin arabinoside PET uptake distributions in lung tumours during radiation therapy. Acta Oncol. 56(4), 516–524 (2017)
Simard, P.Y., Steinkraus, D., Platt, J.C., et al.: Best practices for convolutional neural networks applied to visual document analysis. ICDAR 3, 958–962 (2003)
Dutilleux, P.: An implementation of the “algorithme à trous” to compute the wavelet transform. In: Combes, J.M., Grossmann, A., Tchamitchian, P. (eds.) Wavelets, pp. 298–304. Springer, Heidelberg (1990). https://doi.org/10.1007/978-3-642-75988-8_29
Wang, P., et al.: Understanding convolution for semantic segmentation. arXiv preprint arXiv:1702.08502 (2017)
Van Den Oord, A., et al.: Wavenet: a generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016)
Chollet, F., et al.: Keras (2015)
Acknowledgments
Umair Javaid is funded by Fonds de la Recherche Scientifique - FNRS, Télévie grant no. 7.4625.16. Damien Dasnoy is a Research Fellow of FNRS. John A. Lee is a Senior Research Associate with the Belgian FNRS. We thank UCLouvain University hospital Saint-Luc for providing the data. We also thank NVIDIA Corporation for providing Titan X (Pascal) GPUs.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Javaid, U., Dasnoy, D., Lee, J.A. (2018). Multi-organ Segmentation of Chest CT Images in Radiation Oncology: Comparison of Standard and Dilated UNet. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2018. Lecture Notes in Computer Science(), vol 11182. Springer, Cham. https://doi.org/10.1007/978-3-030-01449-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-01449-0_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01448-3
Online ISBN: 978-3-030-01449-0
eBook Packages: Computer ScienceComputer Science (R0)