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Toward automated segmentation for acute ischemic stroke using non-contrast computed tomography

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

Abstract

Purpose

Non-contrast computed tomography (NCCT) is a first-line imaging technique for determining treatment options for acute ischemic stroke (AIS). However, its poor contrast and signal-to-noise ratio limit the diagnosis accuracy for radiologists, and automated AIS lesion segmentation using NCCT also remains a challenge. In this paper, we propose R2U-RNet, a novel model for AIS lesion segmentation using NCCT.

Methods

We used an in-house retrospective NCCT dataset with 261 AIS patients with manual lesion segmentation using follow-up diffusion-weighted images. R2U-RNet is based on an R2U-Net backbone with a novel residual refinement unit. Each input image contains two image channels from separate preprocessing procedures. The proposed model incorporates multiscale focal loss to mitigate the class imbalance problem and to leverage the importance of different levels of details. A proposed noisy-label training scheme is utilized to account for uncertainties in the manual annotations.

Results

The proposed model outperformed several iconic segmentation models in AIS lesion segmentation using NCCT, and our ablation study demonstrated the efficacy of the proposed model. Statistical analysis of segmentation performance revealed significant effects of regional stroke occurrence and side of the stroke, suggesting the importance of region-specific information for automated segmentation, and the potential influence of the hemispheric difference in clinical data.

Conclusion

This study demonstrated the potentials of R2U-RNet model for automated NCCT AIS lesion segmentation. The proposed model can serve as a tool for accelerating AIS diagnoses and improving the treatment quality of AIS patients.

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References

  1. Barber PA, Demchuk AM, Zhang J, Buchan AM, Lancet ASGJT (2000) Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. Lancet 355(9216):1670–1674. https://doi.org/10.1016/S0140-6736(00)02237-6

    Article  CAS  PubMed  Google Scholar 

  2. Von Kummer R, Bourquain H, Bastianello S, Bozzao L, Manelfe C, Meier D, Hacke W (2001) Early prediction of irreversible brain damage after ischemic stroke at CT. Radiology 219(1):95–100

    Article  Google Scholar 

  3. Vilela P, Rowley HAJE (2017) Brain ischemia: CT and MRI techniques in acute ischemic stroke. 96:162-172

  4. Lövblad K-O, Altrichter S, Pereira VM, Vargas M, Gonzalez AM, Haller S, Sztajzel RJJON (2015) Imaging of acute stroke: CT and/or MRI. 42 (1):55–64

  5. Na DG, Kim EY, Ryoo JW, Lee KH, Roh HG, Kim SS, Song IC, Chang K-H (2005) CT sign of brain swelling without concomitant parenchymal hypoattenuation: comparison with diffusion-and perfusion-weighted MR imaging. Radiology 235(3):992–998

    Article  PubMed  Google Scholar 

  6. Allen LM, Hasso AN, Handwerker J, Farid H (2012) Sequence-specific MR imaging findings that are useful in dating ischemic stroke. Radiographics 32(5):1285–1297

    Article  PubMed  Google Scholar 

  7. Furie KL, Jayaraman MV (2018) 2018 Guidelines for the early management of patients with acute ischemic stroke. Stroke 49(3):509–510. https://doi.org/10.1161/STROKEAHA.118.020176

    Article  PubMed  Google Scholar 

  8. Barber PA, Demchuk AM, Zhang J, Buchan AM, Group AS (2000) Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. Lancet 355(9216):1670-1674

  9. Kuang H, Najm M, Chakraborty D, Maraj N, Sohn S, Goyal M, Hill M, Demchuk A, Menon B, Qiu W (2019) Automated ASPECTS on noncontrast CT scans in patients with acute ischemic stroke using machine learning. Am J Neuroradiol 40(1):33–38

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. International conference on medical image computing and computer-assisted intervention. Springer, New York, pp 234–241

    Google Scholar 

  11. Alom MZ, Hasan M, Yakopcic C, Taha T, Asari V (2018) Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation

  12. Jin Q, Meng Z, Sun C, Cui H, 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 

  13. Chen L, Bentley P, Rueckert DJNC (2017) Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. Neuroimage Clin 15:633–643. https://doi.org/10.1016/j.nicl.2017.06.016

    Article  PubMed  PubMed Central  Google Scholar 

  14. Perez Malla CU, Valdes Hernandez MdC, Rachmadi MF, Komura T (2019) Evaluation of enhanced learning techniques for segmenting ischaemic stroke lesions in brain magnetic resonance perfusion images using a convolutional neural network scheme. Front Neuroinform 13:33

    Article  PubMed  PubMed Central  Google Scholar 

  15. Tomita N, Jiang S, Maeder ME, Hassanpour S (2020) Automatic post-stroke lesion segmentation on MR images using 3D residual convolutional neural network. NeuroImage Clin 27:102276. https://doi.org/10.1016/j.nicl.2020.102276

    Article  PubMed  PubMed Central  Google Scholar 

  16. Wang G, Song T, Dong Q, Cui M, Huang N, Zhang S (2020) Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks. Med Image Anal 65:101787

    Article  PubMed  Google Scholar 

  17. Song T, Huang N (2018) Integrated extractor, generator and segmentor for ischemic stroke lesion segmentation. International MICCAI Brainlesion Workshop. Springer, New York, pp 310–318

    Google Scholar 

  18. Abulnaga SM, Rubin J (2018) Ischemic stroke lesion segmentation in CT perfusion scans using pyramid pooling and focal loss. International MICCAI Brainlesion Workshop. Springer, New York, pp 352–363

    Google Scholar 

  19. Clèrigues A, Valverde S, Bernal J, Freixenet J, Oliver A, Lladó X (2019) Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks. Comput Biol Med 115:103487

    Article  PubMed  Google Scholar 

  20. Dolz J, Ayed IB, Desrosiers C (2018) Dense multi-path U-Net for ischemic stroke lesion segmentation in multiple image modalities. International MICCAI Brainlesion Workshop. Springer, New York, pp 271–282

    Google Scholar 

  21. Xue Y, Farhat FG, Boukrina O, Barrett AM, Binder JR, Roshan UW, Graves WW (2020) A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images. NeuroImage Clin 25:102118. https://doi.org/10.1016/j.nicl.2019.102118

    Article  PubMed  Google Scholar 

  22. Zhang J, Lv X, Sun Q, Zhang Q, Wei X, Liu B (2020) SDResU-net: separable and dilated residual U-net for MRI brain tumor segmentation. Curr Med Imag 16(6):720–728

    Article  Google Scholar 

  23. Aygün M, Şahin YH, Ünal G (2018) Multi modal convolutional neural networks for brain tumor segmentation

  24. Fuchigami T, Akahori S, Okatani T, Li Y (2020) A hyperacute stroke segmentation method using 3D U-Net integrated with physicians’ knowledge for NCCT. In: Medical Imaging 2020: Computer-Aided Diagnosis, 2020. International Society for Optics and Photonics, p 113140G

  25. Kuang H, Menon BK, Sohn SI, Qiu W (2021) EIS-Net: segmenting early infarct and scoring ASPECTS simultaneously on non-contrast CT of patients with acute ischemic stroke. Med Image Anal 70:101984

    Article  PubMed  Google Scholar 

  26. Qiu W, Kuang H, Teleg E, Ospel JM, Sohn SI, Almekhlafi M, Goyal M, Hill MD, Demchuk AM, Menon BK (2020) Machine learning for detecting early infarction in acute stroke with non–contrast-enhanced CT. Radiology 294(3):638–644

    Article  PubMed  Google Scholar 

  27. Kuang H, Menon BK, Qiu W (2020) Automated stroke lesion segmentation in non-contrast CT scans using dense multi-path contextual generative adversarial network. Phys Med Biol 65(21):215013

    Article  PubMed  Google Scholar 

  28. Kuang H, Menon BK, Qiu W (2019) Semi-automated infarct segmentation from follow-up noncontrast CT scans in patients with acute ischemic stroke. Med Phys 46(9):4037–4045

    Article  PubMed  Google Scholar 

  29. Tuladhar A, Schimert S, Rajashekar D, Kniep HC, Fiehler J, Forkert ND (2020) Automatic segmentation of stroke lesions in non-contrast computed tomography datasets with convolutional neural networks. IEEE Access 8:94871–94879

    Article  Google Scholar 

  30. Mirikharaji Z, Yan Y, Hamarneh G (2019) Learning to segment skin lesions from noisy annotations. In: Wang Q, Milletari F, Nguyen HV et al (eds) Domain adaptation and representation transfer and medical image learning with less labels and imperfect data, 2019. Springer, Cham, pp 207–215

    Google Scholar 

  31. Sukhbaatar S, Bruna J, Paluri M, Bourdev L, Fergus R (2014) Training convolutional networks with noisy labels

  32. Tajbakhsh N, Jeyaseelan L, Li Q, Chiang JN, Wu Z, Ding X (2020) Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med Image Anal 63:101693. https://doi.org/10.1016/j.media.2020.101693

    Article  PubMed  Google Scholar 

  33. Hazimeh H, Ponomareva N, Mol P, Tan Z, Mazumder R (2020) The tree ensemble layer: differentiability meets conditional computation

  34. Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, ter Haar RB, Zimmerman JB, Zuiderveld K (1987) Adaptive histogram equalization and its variations. Comput Vis Graph Image Process 39(3):355–368

    Article  Google Scholar 

  35. Lai W-S, Huang J-B, Ahuja N, Yang M-H (2018) Fast and accurate image super-resolution with deep laplacian pyramid networks. IEEE Trans Pattern Anal Mach Intell 41(11):2599–2613

    Article  PubMed  Google Scholar 

  36. Lin T-Y, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp 2980–2988

  37. Clèrigues A, Valverde S, Bernal J, Freixenet J, Oliver A, Lladó X (2020) Acute and sub-acute stroke lesion segmentation from multimodal MRI. Comput Methods Prog Biomed 194:105521

    Article  Google Scholar 

  38. Zhou Y, Huang W, Dong P, Xia Y, Wang S (2019) D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation. IEEE/ACM Trans Comput Biol Bioinform

  39. Liu L, Kurgan L, Wu F-X, Wang J (2020) Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease. Med Image Anal 65:101791

    Article  PubMed  Google Scholar 

  40. Jaccard P (1901) Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines. Bull Soc Vaudoise Sci Nat 37:241–272

    Google Scholar 

  41. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302

    Article  Google Scholar 

  42. Yeghiazaryan V, Voiculescu ID (2018) Family of boundary overlap metrics for the evaluation of medical image segmentation. J Med Imag 5(1):015006

    Article  Google Scholar 

  43. Ashburner J, Barnes G, Chen C, Daunizeau J, Flandin G, Friston K, Kiebel S, Kilner J, Litvak V, Moran RJWTCfN, London, UK (2014) SPM12 manual.2464

  44. Macciocchi SN, Diamond PT, Alves WM, Mertz T (1998) Ischemic stroke: relation of age, lesion location, and initial neurologic deficit to functional outcome. Arch Phys Med Rehabil 79(10):1255–1257. https://doi.org/10.1016/S0003-9993(98)90271-4

    Article  CAS  PubMed  Google Scholar 

  45. Foerch C, Misselwitz B, Sitzer M, Berger K, Steinmetz H, Neumann-Haefelin T (2005) Difference in recognition of right and left hemispheric stroke. Lancet 366(9483):392–393. https://doi.org/10.1016/S0140-6736(05)67024-9

    Article  PubMed  Google Scholar 

  46. Portegies ML, Selwaness M, Hofman A, Koudstaal PJ, Vernooij MW, Ikram MA (2015) Left-sided strokes are more often recognized than right-sided strokes: the Rotterdam study. Stroke 46(1):252–254

    Article  PubMed  Google Scholar 

  47. Group ES (1996) Silent brain infarction in nonrheumatic atrial fibrillation: European Atrial Fibrillation Trial. Neurology 46:159-165

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Funding

This work was supported in part by the Kaohsiung Chang Gung Memorial Hospital (CMRPG8H0951 and CMRPG8K0131), the Higher Education Sprout Project of National Yang Ming Chiao Tung University and Ministry of Education (MOE), and Ministry of Science and Technology (MOST 110-2634-F-A49-005), Taiwan.

Author information

Author notes

  1. Shih-Yen Lin and Pi-Ling Chiang contributed equally to this work and shared the role of first author.

Authors and Affiliations

  1. Department of Computer Science, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, Taiwan

    Shih-Yen Lin, Li-Hsin Cheng, Pei-Chun Chang & Yong-Sheng Chen

  2. Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, 123 Ta-Pei Road, Niao-Sung, Kaohsiung, 83305, Taiwan

    Pi-Ling Chiang, Meng-Hsiang Chen & Wei-Che Lin

  3. Institute of Biomedical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Peng-Wen Chen

  4. Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands

    Li-Hsin Cheng

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Correspondence to Wei-Che Lin or Yong-Sheng Chen.

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The authors have no conflict of interest to disclose.

Ethical approval

This study was carried out in accordance with the recommendations of the Institutional Review Board of our hospital. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The protocol was approved by the research ethics committee of Kaohsiung Chang Gung Memorial Hospital (IRB#: 201800955B0, 201800955B0C101, and 201800955B0C102).

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Lin, SY., Chiang, PL., Chen, PW. et al. Toward automated segmentation for acute ischemic stroke using non-contrast computed tomography. Int J CARS 17, 661–671 (2022). https://doi.org/10.1007/s11548-022-02570-x

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  • DOI: https://doi.org/10.1007/s11548-022-02570-x

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