Abstract
Automated detection of anterior mediastinal nodular lesions (AMLs) has significance for clinical usage as it is challenging for radiologists to accurately identify AMLs from chest computed tomography (CT) imaging due to various factors, including poor resolution, variations in intensity and the similarity of the AMLs to other tissues. To assist radiologists in AML detection from chest CT imaging, a UNet-based computer-aided detection (CADe) system is proposed to segment AMLs from slice images of the chest CT scans. The proposed network adopts a modified UNet architecture. To guide the proposed network to selectively focus on AMLs and potentially disregard others in the image, different attention mechanisms are utilized in the proposed network, including the self-attention mechanism and the convolutional block attention module (CBAM). The proposed network was trained and evaluated on 180 chest CT scans which consist of 180 AMLs. 90 AMLs were identified as thymic cysts, and 90 AMLs were diagnosed as thymoma. The proposed network achieved an average dice similarity coefficient (DSC) of 93.23 with 5-fold cross-validation, for which the mean Intersection over Union (IoU), sensitivity and specificity were 90.29, 93.98 and 95.68 respectively. Our method demonstrated an improved segmentation performance over state-of-the-art segmentation networks, including UNet, ResUNet, TransUNet and UNet++. The proposed network employing attention mechanisms exhibited a promising result for segmenting AMLs from chest CT imaging and could be used to automate the AML detection process for achieving improved diagnostic reliability.
Similar content being viewed by others
References
de Koning HJ, van der Aalst CM, de Jong PA, Scholten ET, Nackaerts K, Heuvelmans MA, Lammers JWJ, Weenink C, Yousaf-Khan U, Horeweg N, van’t Westeinde S, Prokop M, Mali WP, Mohamed Hoesein FA, van Ooijen PM, Aerts JG, den Bakker MA, Thunnissen E, Verschakelen J, Vliegenthart R, Walter JE, ten Haaf K, Groen HJ, Oudkerk M (2020) Reduced lung-cancer mortality with volume ct screening in a randomized trial. N Engl J Med 382(6):503–513. https://doi.org/10.1056/nejmoa1911793
Yoon SH, Choi SH, Kang CH, Goo JM (2018) Incidental anterior mediastinal nodular lesions on chest CT in asymptomatic subjects. J Thorac Oncol 13(3):359–366. https://doi.org/10.1016/j.jtho.2017.11.124
Munden RF, Carter BW, Chiles C, MacMahon H, Black WC, Ko JP, McAdams HP, Rossi SE, Leung AN, Boiselle PM, Kent MS, Brown K, Dyer DS, Hartman TE, Goodman EM, Naidich DP, Kazerooni EA, Berland LL, Pandharipande PV (2018) Managing incidental findings on thoracic CT: Mediastinal and cardiovascular findings. A white paper of the ACR incidental findings committee. J Am Coll Radiol 15(8):1087–1096. https://doi.org/10.1016/j.jacr.2018.04.029
Bailey CR, Bailey AM, McKenney AS, Weiss CR (2022) Understanding and appreciating burnout in radiologists. Radiographics 42(5):E137–E139. https://doi.org/10.1148/rg.220037
Roth HR, Lu L, Liu J, Yao J, Seff A, Cherry K, Kim L, Summers RM (2016) Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans Med Imaging 35(5):1170–1181. https://doi.org/10.1109/TMI.2015.2482920
Castro-Zunti R, Park EH, Choi Y, Jin GY, Bum Ko S (2020) Early detection of ankylosing spondylitis using texture features and statistical machine learning, and deep learning, with some patient age analysis. Comput Med Imaging Graph 82:101–718. https://doi.org/10.1016/j.compmedimag.2020.101718
Haghanifar A, Majdabadi MM, Choi Y, Deivalakshmi S, Ko S (2022) COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning. Multimed Tools Appl 81(21):30615–30645. https://doi.org/10.1007/s11042-022-12156-z
Jung W, Cho S, Yum S, Lee YK, Kim K, Jheon S (2020) Differentiating thymoma from thymic cyst in anterior mediastinal abnormalities smaller than 3 cm. J Thor Dis 12(4):1357–1365. https://doi.org/10.21037/jtd.2020.02.14
Sandor T, Metcalf D, Kim YJ (1991) Segmentation of brain CT images using the concept of region growing. Int J Bio-Med Comput 29(2):133–147. https://doi.org/10.1016/0020-7101(91)90004-X
Ye X, Beddoe G, Slabaugh G (2010) Automatic graph cut segmentation of lesions in CT using mean shift superpixels. International Journal of Biomedical Imaging 2010. https://doi.org/10.1155/2010/983963
Hemalatha R, Thamizhvani T, Dhivya AJA, Joseph JE, Babu B, Chandrasekaran R (2018) Active contour based segmentation techniques for medical image analysis. Medical and Biological Image Analysis. https://doi.org/10.5772/intechopen.74576
Long J, Shelhamer E, Darrell T (2014) Fully convolutional networks for semantic segmentation. arXiv:1411.4038
Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional networks for biomedical image segmentation. arXiv:1505.04597
Aresta G, Jacobs C, Araújo T, Cunha A, Ramos I, van Ginneken B, Campilho A (2019) iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network. Sci Rep 9(1):1–9. https://doi.org/10.1038/s41598-019-48004-8
Usman M, Lee BD, Byon SS, Kim SH, Il Lee B, Shin YG (2020) Volumetric lung nodule segmentation using adaptive ROI with multi-view residual learning. Sci Rep 10(1):1–15. https://doi.org/10.1038/s41598-020-69817-y
Oda H, Bhatia KK, Roth HR, Oda M, Kitasaka T, Iwano S, Homma H, Takabatake H, Mori M, Natori H, Schnabel JA, Mori K (2018) Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images. Proc SPIE 10575:1. https://doi.org/10.1117/12.2287066
Nayan AA, Kijsirikul B, Iwahori Y (2022) Mediastinal lymph node detection and segmentation using deep learning. IEEE Access 10:89289–89307. https://doi.org/10.1109/ACCESS.2022.3198996
Huang S, Han X, Fan J, Chen J, Du L, Gao W, Liu B, Chen Y, Liu X, Wang Y, Ai D, Ma G, Yang J (2021) Anterior mediastinal lesion segmentation based on two-stage 3D ResUNet with attention gates and lung segmentation. FronT Oncol 10:3290. https://doi.org/10.3389/fonc.2020.618357
Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, Glocker B, Rueckert D (2018) Attention U-Net: Learning where to look for the pancreas. arXiv:1804.03999
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. arXiv:1706.03762
Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y (2021) TransUNet: Transformers make strong encoders for medical image segmentation. arXiv:2102.04306
Zhang DJ, Li K, Wang Y, Chen Y, Chandra S, Qiao Y, Liu L, Shou MZ (2022) MorphMLP: An efficient MLP-like backbone for spatial-temporal representation learning. arXiv:2111.12527
Zhang Z, Liu Q, Wang Y (2018) Road extraction by deep residual U-Net. IEEE Geosci Remote Sens Lett 15(5):749–753. https://doi.org/10.1109/LGRS.2018.2802944
Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J (2018) Unet++: A nested u-net architecture for medical image segmentation. Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 11045 LNCS, pp 3–11. https://doi.org/10.1007/978-3-030-00889-5_1
Choe J, Lee SM, Ahn Y, Kim CH, Seo JB, Lee HY (2022) Characteristics and outcomes of anterior mediastinal cystic lesions diagnosed on chest MRI: implications for management of cystic lesions. Insights Imaging 13(1):1–12. https://doi.org/10.1186/s13244-022-01275-8
Ackman JB, Chintanapakdee W, Mendoza DP, Price MC, Lanuti M, Shepard JAO (2021) Longitudinal CT and MRI characteristics of unilocular thymic cysts. Radiol 301(2):443–454. https://doi.org/10.1148/radiol.2021203593
Kikinis R, Pieper SD, Vosburgh KG (2014) 3D slicer: A Platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy, pp 277–289. https://doi.org/10.1007/978-1-4614-7657-3_19
Wang W, Xie E, Li X, Fan D, Song K, Liang D, Lu T, Luo P, Shao L (2021) PVTv2: Improved baselines with pyramid vision transformer. arXiv:2106.13797
Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. arXiv:1607.06450
Yu F, Koltun V (2016) Multi-scale context aggregation by dilated convolutions. In: 4th International Conference on Learning Representations, ICLR 2016 - Conference track proceedings . arXiv:1511.07122
Woo S, Park J, Lee JY, Kweon IS (2018) CBAM: Convolutional block attention module. Lecture Notes in Computer Science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) 11211 LNCS, pp 3–19. https://doi.org/10.1007/978-3-030-01234-2_1
Hu J, Shen L, Albanie S, Sun G, Wu E (2020) Squeeze-and-excitation networks. IEEE Tran Pattern Anal Mach Intell 42(8):2011–2023. https://doi.org/10.1109/TPAMI.2019.2913372
Milletari F, Navab N, Ahmadi SA (2016) V-Net: Fully convolutional neural networks for volumetric medical image segmentation, pp 565–571. https://doi.org/10.1109/3DV.2016.79
Tomar NK, Shergill A, Rieders B, Bagci U, Jha D (2022) Transresu-net: Transformer based resu-net for real-time colonoscopy polyp segmentation. arXiv:2206.08985
Tong G, Li Y, Chen H, Zhang Q, Jiang H (2018) Improved U-NET network for pulmonary nodules segmentation. Optik 174:460–469. https://doi.org/10.1016/j.ijleo.2018.08.086
Sui H, Liu L, Li X, Zuo P, Cui J, Mo Z (2019) CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions. J Thor Dis 11(5):1809–1818. https://doi.org/10.21037/jtd.2019.05.32
Yang L, Cai W, Yang X, Zhu H, Liu Z, Wu X, Lei Y, Zou J, Zeng B, Tian X, Zhang R, Luo H, Zhu Y (2020) Development of a deep learning model for classifying thymoma as Masaoka-Koga stage I or II via preoperative CT images. Annals Trans Med 8(6):287–287. https://doi.org/10.21037/atm.2020.02.183
Liu Z, Zhu Y, Yuan Y, Yang L, Wang K, Wang M, Yang X, Wu X, Tian X, Zhang R, Shen B, Luo H, Feng H, Feng S, Ke Z (2021) 3D densenet deep learning based preoperative computed tomography for detecting myasthenia gravis in patients with thymoma. Frontiers in Oncology 11. https://doi.org/10.3389/fonc.2021.631964
Linsley D, Kim J, Veerabadran V, Serre T (2018) Learning long-range spatial dependencies with horizontal gated-recurrent units. arXiv:1805.08315
Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, Van Riel SJ, Wille MMW, Naqibullah M, Sanchez CI, Van Ginneken B (2016) Pulmonary nodule detection in CT images: False positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35(5):1160–1169. https://doi.org/10.1109/TMI.2016.2536809
...Armato SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Van Beek EJ, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DP, Roberts RY, Smith AR, Starkey A, Batra P, Caligiuri P, Farooqi A, Gladish GW, Jude CM, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Vande Casteele A, Gupte S, Sallam M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY, Clarke LP (2011) The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Med Phys 38(2):915–931. https://doi.org/10.1118/1.3528204
Acknowledgements
The authors would like to thank the Natural Sciences and Engineering Research Council (NSERC) of Canada and the Department of Electrical and Computer Engineering at the University of Saskatchewan for their financial support for this research work.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yi Wang and Won Gi Jeong contributed equally to this work.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wang, Y., Jeong, W.G., Zhang, H. et al. Anterior mediastinal nodular lesion segmentation from chest computed tomography imaging using UNet based neural network with attention mechanisms. Multimed Tools Appl 83, 45969–45987 (2024). https://doi.org/10.1007/s11042-023-17210-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-17210-y