Abstract
Recent studies have emphasized the importance of protecting thoracic duct during radiation therapy (RT), as dose distributions in thoracic duct may be associated with the development radiation-induced lymphopenia. Because of its thin/slim size, curved geometry and extremely poor (intensity) contrast of thoracic duct, manual delineation of thoracic duct in RT planning CT is time-consuming and with large inter-observer variations. In this work, we aim to automatically and accurately segment thoracic duct in RT planning CT, as the first attempt to tackle this clinically critical yet under-studied task. A two-stage coarse-to-fine segmentation approach is proposed. At the first stage, we automatically segment six chest organs and combine these organ predictions with the input planning CT to better infer and localize the thoracic duct. Given the coarse initial segmentation from first stage, we subsequently extract the topology-corrected centerline of initial thoracic duct segmentation at stage two where curved planar reformation (CPR) is applied to transform the planning CT into a new 3D volume representation that provides a spatially smoother reformation of thoracic duct in its elongated medial axis direction. Thus the CPR-transformed CT is employed as input to the second stage deep segmentation network, and the output segmentation mask is transformed back to the original image space, as the final segmentation. We evaluate our approach on 117 lung cancer patients with RT planning CT scans. Our approach significantly outperforms a strong baseline model based on nnUNet, by reducing 57% relative Hausdorff distance error (from 49.9 mm to 21.2 mm) and improving 1.8% absolute Jaccard Index.
P. Wang, P. Hu, J. Liu—Equal contribution.
D. Jin, F.-M. S. Kong—Co-senior author.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, X., et al.: A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy. Radiother. Oncol. 160, 175–184 (2021)
Davuluri, R., et al.: Absolute lymphocyte count nadir during chemoradiation as a prognostic indicator of esophageal cancer survival outcomes. Int. J. Radiat. Oncol. Biol. Phys. 96(2), E177 (2016)
Guo, D., et al.: Organ at risk segmentation for head and neck cancer using stratified learning and neural architecture search. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4223–4232 (2020)
Guo, D., et al.: DeepStationing: thoracic lymph node station parsing in CT scans using anatomical context encoding and key organ auto-search. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 3–12. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87240-3_1
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: NNU-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Ji, Z., et al.: Continual segment: towards a single, unified and accessible continual segmentation model of 143 whole-body organs in CT scans. In: IEEE International Conference on Computer Vision (2023)
Jin, D., Guo, D., Ge, J., Ye, X., Lu, L.: Towards automated organs at risk and target volumes contouring: defining precision radiation therapy in the modern era. J. Natl. Can. Center 2, 306–313 (2022)
Jin, D., et al.: Deeptarget: gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy. Med. Image Anal. 68, 101909 (2021)
Jin, D., Iyer, K.S., Chen, C., Hoffman, E.A., Saha, P.K.: A robust and efficient curve skeletonization algorithm for tree-like objects using minimum cost paths. Pattern Recogn. Lett. 76, 32–40 (2016)
Jin, J.Y., et al.: A framework for modeling radiation induced lymphopenia in radiotherapy. Radiother. Oncol. 144, 105–113 (2020)
Kanitsar, A., Fleischmann, D., Wegenkittl, R., Felkel, P., Groller, E.: CPR-curved planar reformation. IEEE (2002)
Kanitsar, A., Wegenkittl, R., Fleischmann, D., Groller, M.E.: Advanced curved planar reformation: flattening of vascular structures. IEEE (2003)
Kiyonaga, M., Mori, H., Matsumoto, S., Yamada, Y., Sai, M., Okada, F.: Thoracic duct and cisterna chyli: evaluation with multidetector row CT. Br. J. Radiol. 85(1016), 1052–1058 (2012)
Lee, T.C., Kashyap, R.L., Chu, C.N.: Building skeleton models via 3-d medial surface axis thinning algorithms. CVGIP Graphical Models Image Process. 56(6), 462–478 (1994)
Liu, J., et al.: Integrate sequence information of dose volume histogram in training LSTM-based deep learning model for lymphopenia diagnosis. Int. J. Radiat. Oncol. Biol. Phys. 111(3), e112–e113 (2021)
Schnyder, P., et al.: CT of the thoracic duct. Eur. J. Radiol. 3(1), 18–23 (1983)
Shi, F.: Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy. Nat. Commun. 13(1), 6566 (2022)
Siegel, R.L., Miller, K.D., Fuchs, H.E., Jemal, A.: Cancer statistics, 2022. CA Can. J. Clin. 72(1), 7–33 (2022)
So, T.H., et al.: Lymphopenia and radiation dose to circulating lymphocytes with neoadjuvant chemoradiation in esophageal squamous cell carcinoma. Adv. Radiat. Oncol. 5(5), 880–888 (2020)
Tang, C., et al.: Lymphopenia association with gross tumor volume and lung v5 and its effects on non-small cell lung cancer patient outcomes. Int. J. Radiat. Oncol. Biol. Phys. 89(5), 1084–1091 (2014)
Tang, H., et al.: Clinically applicable deep learning framework for organs at risk delineation in CT images. Nat. Mach. Intell. 1(10), 480–491 (2019)
Tyldesley, S., Boyd, C., Schulze, K., Walker, H., Mackillop, W.J.: Estimating the need for radiotherapy for lung cancer: an evidence-based, epidemiologic approach. Int. J. Radiat. Oncol. Biol. Phys. 49(4), 973–985 (2001)
Wang, P., et al.: Accurate airway tree segmentation in ct scans via anatomy-aware multi-class segmentation and topology-guided iterative learning. arXiv preprint arXiv:2306.09116 (2023)
Xu, C., et al.: The impact of the effective dose to immune cells on lymphopenia and survival of esophageal cancer after chemoradiotherapy. Radiother. Oncol. 146, 180–186 (2020)
Ye, X., et al.: Comprehensive and clinically accurate head and neck cancer organs-at-risk delineation on a multi-institutional study. Nat. Commun. 13(1), 6137 (2022)
Zhu, Z., et al.: Lymph node gross tumor volume detection and segmentation via distance-based gating using 3D CT/PET imaging in radiotherapy. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 753–762. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59728-3_73
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, P. et al. (2024). Automated Coarse-to-Fine Segmentation of Thoracic Duct Using Anatomy Priors and Topology-Guided Curved Planar Reformation. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14348. Springer, Cham. https://doi.org/10.1007/978-3-031-45673-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-031-45673-2_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45672-5
Online ISBN: 978-3-031-45673-2
eBook Packages: Computer ScienceComputer Science (R0)
