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Coarse-to-fine multiplanar D-SEA UNet for automatic 3D carotid segmentation in CTA images

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Carotid artery atherosclerotic stenosis accounts for 18–25% of ischemic stroke. In the evaluation of carotid atherosclerotic lesions, the automatic, accurate and rapid segmentation of the carotid artery is a priority issue that needs to be addressed urgently. However, the carotid artery area occupies a small target in computed tomography angiography (CTA) images, which affect the segmentation accuracy.

Methods

We proposed a coarse-to-fine segmentation pipeline with the Multiplanar D-SEA UNet to achieve fully automatic carotid artery segmentation on the entire 3D CTA images, and compared with other four neural networks (3D-UNet, RA-UNet, Isensee-UNet, Multiplanar-UNet) by assessing Dice, Jaccard similarity coefficient, sensitivity, area under the curve and average hausdorff distance.

Results

Our proposed method can achieve a mean Dice score of 91.51% on the 68 neck CTA scans from Beijing Hospital, which remarkably outperforms state-of-the-art 3D image segmentation methods. And the C2F segmentation pipeline can effectively improve segmentation accuracy while avoiding resolution loss.

Conclusion

The proposed segmentation method can realize the fully automatic segmentation of the carotid artery and has robust performance with segmentation accuracy, which can be applied into plaque exfoliation and interventional surgery services. In addition, our method is easy to extend to other medical segmentation tasks with appropriate parameter settings.

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Abbreviations

CTA:

Computed tomography angiography

CNN:

Convolutional neural network

DSA:

Digital subtraction angiography

ECAD:

Extracranial atherosclerotic disease

C2F:

Coarse-to-fine

Sen:

Sensitivity

AUC:

Area under the curve

AVD:

Average Hausdorff distance

References

  1. Collaborators G2 (2018) Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet (London, England) 392:1859–1922

    Article  Google Scholar 

  2. Brott T, Halperin J, Abbara S, Bacharach J, Barr J, Bush R, Cates CU, Creager M, Fowler SB, Friday G, Hertzberg V, McIff EB, Moore W, Panagos P, Riles T, Rosenwasser R, Taylor A (2011) 2011 ASA/ACCF/AHA/AANN/AANS/ACR/ASNR/CNS/SAIP/SCAI/SIR/SNIS/SVM/SVS guideline on the management of patients with extracranial carotid and vertebral artery disease: executive summary. Stroke 42(8):e420–e463

    PubMed  Google Scholar 

  3. Ooi Y, Gonzalez N (2015) Management of extracranial carotid artery disease. Cardiol Clin 33(1):1–35

    Article  Google Scholar 

  4. Saba L, Saam T, Jäger H, Yuan C, Wintermark M (2019) Imaging biomarkers of vulnerable carotid plaques for stroke risk prediction and their potential clinical implications. Lancet Neurol 18:559–572

    Article  Google Scholar 

  5. Saxena A, Ng E, Lim S (2019) Imaging modalities to diagnose carotid artery stenosis: progress and prospect. BioMed Eng Online 18:1–23

    Article  Google Scholar 

  6. Beare R, Chong W, Ren M, Das G, Srikanth V, Phan T (2010) Segmentation of carotid arteries in CTA images. Int Conf Digital Image Comput Tech Appl 2010:69–74

    Google Scholar 

  7. Santos FL, Joutsen A, Terada M, Salenius J, Eskola H (2014) A semi-automatic segmentation method for the structural analysis of carotid atherosclerotic plaques by computed tomography angiography. J Atheroscler Thromb 21(9):930–940

    Article  Google Scholar 

  8. Bozkurt F, Köse C, Sarı A (2018) An inverse approach for automatic segmentation of carotid and vertebral arteries in CTA. Expert Syst Appl 93:358–375

    Article  Google Scholar 

  9. Tang H, Walsum TV, Hameeteman R, Shahzad R, Vliet LV, Niessen W (2013) Lumen segmentation and stenosis quantification of atherosclerotic carotid arteries in CTA utilizing a centerline intensity prior. Med Phys 40(5):051721

    Article  Google Scholar 

  10. Zhou R, Fenster A, Xia Y, Spence J, Ding M (2019) Deep learning-based carotid media-adventitia and lumen-intima boundary segmentation from three-dimensional ultrasound images. Med Phys 46:3180–3193

    Article  Google Scholar 

  11. Moreira AS (2019) Carotid lumen segmentation using a neural network approach. Dissertation, University of Porto

  12. Pan XY (2019) Carotid bifurcation segmentation based on the deep neural network UNet. Dissertation, Zhejiang University

  13. Hameeteman R, Zuluaga MA, Freiman M, Joskowicz L, Cuisenaire O, Florez-Valencia L, Gülsün M, Krissian K, Mille J, Wong WC, Orkisz M, Tek H, Hoyos MH, Benmansour F, Chung AC, Rozie S, Gils MV, Borne LV, Sosna J, Berman P, Cohen N, Douek P, Sánchez I, Aissat M, Schaap M, Metz C, Krestin G, Lugt A, Niessen W, Walsum TV (2011) Evaluation framework for carotid bifurcation lumen segmentation and stenosis grading. Med Image Anal 15(4):477–488

    Article  CAS  Google Scholar 

  14. Ziegler M, Alfraeus J, Bustamante M, Good E, Engvall J, de Muinck E, Dyverfeldt P (2021) Automated segmentation of the individual branches of the carotid arteries in contrast-enhanced MR angiography using DeepMedic. BMC Med Imag 21(1):1–10

    Article  Google Scholar 

  15. Perslev M, Dam E, Pai A, Igel C (2019) One network to segment them all: a general, lightweight system for accurate 3D medical image segmentation. In: MICCAI, pp 30–38

  16. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A (eds) Medical image computing and computer-assisted intervention – MICCAI 2015. MICCAI 2015. Lecture notes in computer science, vol 9351, Springer, pp 234–241

  17. Isensee F, Kickingereder P, Wick W, Bendszus M, Maier-Hein KH (2018) Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge. In: Crimi A, Bakas S, Kuijf H, Menze B, Reyes M (eds) Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes 2017. Lecture notes in computer science, vol 10670, Springer, pp 287–297

  18. Roy AG, Navab N, Wachinger C (2019) Recalibrating fully convolutional networks with spatial and channel “squeeze and excitation” blocks. IEEE Trans Med Imag 38:540–549

    Article  Google Scholar 

  19. Oktay O, Schlemper J, Folgoc LL, Lee MJ, Heinrich M, Misawa K, Mori K, McDonagh SG, Hammerla N, Kainz B, Glocker B, Rueckert D (2018). Attention U-Net: learning where to look for the pancreas. http://arxiv.org/abs/1804.03999.

  20. Castro E, Cardoso JS, Pereira JC (2018) Elastic deformations for data augmentation in breast cancer mass detection. IEEE EMBS Int Conf Biomed Health Inform (BHI) 2018:230–234

    Google Scholar 

  21. Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imag 15(1):29

    Article  Google Scholar 

  22. Powers D (2011) Evaluation: from precision, recall and F-factor to ROC, informedness, markedness correlation. Mach Learn Technol 2:37–63

    Google Scholar 

  23. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin S, Joskowicz L, Sabuncu M, Unal G, Wells W (eds) Medical image computing and computer-assisted intervention – MICCAI 2016. MICCAI 2016. Lecture notes in computer science, vol 9901, Springer, pp 424–432

  24. Jin Q, Meng Z, Sun C, Wei L, Su R (2020) RA-UNet: a hybrid deep attention-aware network to extract liver and tumor in CT scans. Front Bioeng Biotechnol 8:1471

    Google Scholar 

  25. Kou C, Li W, Liang W, Yu Z, Hao J (2019) Microaneurysms segmentation with a U-Net based on recurrent residual convolutional neural network. J Med Imag 6:025008–025008

    Article  Google Scholar 

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Funding

This work was funded by the Capital’s Funds for Health Improvement and Research (2020-4-4053), the National Natural Science Foundation of China (KKA309004533), the Enterprise funded projects from Fudan University (20275), the Independent Research fund of Key Laboratory of Industrial Dust Prevention and Control & Occupational Health and Safety, Ministry of Education (Anhui University of Science and Technology) (EK20201003) and the Shandong Key Laboratory of Intelligent Buildings Technology (SDIBT202006).

Author information

Author notes

  1. Junjie Wang, Yuanyuan Yu and Rongyao Yan have contributed equally to this work.

Authors and Affiliations

  1. Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, No. 1 Dahua Road, Dongcheng District, Beijing, 100730, China

    Junjie Wang

  2. Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, No. 1 Dahua Road, Dongcheng District, Beijing, China

    Junjie Wang

  3. Department of Biomedical Engineering, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China

    Yuanyuan Yu, Rongyao Yan & Jie Liu

  4. Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, 200040, China

    Hao Wu

  5. Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China

    Zekuan Yu

  6. Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China

    Daoying Geng & Zekuan Yu

  7. Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Research, Shanghai, China

    Junjie Wang, Daoying Geng & Zekuan Yu

  8. Key Laboratory of Industrial Dust Prevention and Control and Occupational Health and Safety, Ministry of Education, Anhui, China

    Junjie Wang & Zekuan Yu

  9. Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui, China

    Junjie Wang & Zekuan Yu

  10. Key Laboratory of Industrial Dust Deep Reduction and Occupational Health and Safety of Anhui Higher Education Institutes, Anhui, China

    Zekuan Yu

Authors
  1. Junjie Wang
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  2. Yuanyuan Yu
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  4. Jie Liu
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Contributions

JW collected and labeled the medical images. YY and RY conducted the experiments, wrote the draft. HW and DG participated in data and material review. JL and ZY participated in experiment design, reviewed and edited the manuscript.

Corresponding authors

Correspondence to Jie Liu or Zekuan Yu.

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Informed consent was obtained from all individual participants included in the study.

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Wang, J., Yu, Y., Yan, R. et al. Coarse-to-fine multiplanar D-SEA UNet for automatic 3D carotid segmentation in CTA images. Int J CARS 16, 1727–1736 (2021). https://doi.org/10.1007/s11548-021-02471-5

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  • DOI: https://doi.org/10.1007/s11548-021-02471-5

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