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Adjusting U-Net for the aortic abdominal aneurysm CT segmentation case
R.U. Epifanov 1, N.A. Nikitin 2, A.A. Rabtsun 3, L.N. Kurdyukov 3, A.A. Karpenko 2, R.I. Mullyadzhanov 1,4

1 Novosibirsk State University, 63090, Russia, Novosibirsk, Pirogov str., 2;
2 Meshalkin National Medical Research Center, 630055, Russia, Novosibirsk, Rechkunovskaya str., 15;
3 Novosibirsk State Medical University, 630091, Russia, Novosibirsk, Krasny ave., 52;
4 Institute of Thermophysics SB RAS, 630090, Russia, Novosibirsk, Lavrentyev ave., 1

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DOI: 10.18287/2412-6179-CO-1338

Pages: 418-424.

Full text of article: English language.

Abstract:
In this paper, we address the issue of developing of a convolutional neural network for the problem of aneurysm segmentation into three classes and of exploring ways for improving the quality of final segmentation masks. As a result of our study, macro dice score for classes of interest reaches 83.12% ± 4.27%. We explored different augmentation styles and showed the importance of applying intensity augmentation style to improve segmentation algorithm robustness in conditions of clinical data diversity. Augmentation with spatial and insensitive styles increase macro dice score up to 3%. The comparison of various inference mode indicate that combination of overlapping inference and segmentation window enlargement ameliorate macro dice up to 1.4%. Overall improvement of the quality of segmentation masks by macro dice score amounted up to 6% using combination of data-based augmentation style and advanced inference technique.

Keywords:
abdominal aortic aneurysm, neural network, semantic segmentation, calcifications.

Citation:
Epifanov RU, Nikitin NA, Rabtsun AA, Kurdyukov LN, Karpenko AA, Mullyadzhanov RI. Adjusting U-Net for the aortic abdominal aneurysm CT segmentation case. Computer Optics 2024; 48(3): 418-424. DOI: 10.18287/2412-6179-CO-1338.

Acknowledgements:
The work is supported by the Russian Science Foundation grant No. 21-15-00091.

References:
  1. Ernst CB. Abdominal aortic aneurysm. N Engl J Med 1993; 328(16): 1167-1172.
  2. Pecoraro F, et al. Mortality rates and risk factors for emergent open repair of abdominal aortic aneurysms in the endovascular era. Updates Surg 2018; 70(1): 129-136.
  3. Sharafuddin MJ, Man JH. Management of aortic aneurysms. In Book: Shammas NW, ed. Peripheral arterial interventions. Springer; 2022: 309-318.
  4. Wanhainen A, et al. European Society for Vascular Surgery (ESVS) 2019 clinical practice guidelines on the management of abdominal aorto-iliac artery aneurysms. Acta Angiol 2022; 28(3): 69-146.
  5. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. Int Conf on Medical Image Computing and Computer-Assisted Intervention 2015: 234-241.
  6. Lu J-T, et al. DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning. Int Conf on Medical Image Computing and Computer-Assisted Intervention 2019: 723-731.
  7. Habijan M, et al. Abdominal aortic aneurysm segmentation from ct images using modified 3D U-Net with deep supervision. 2020 Int Symp ELMAR 2020: 123-128.
  8. Brutti F, et al. Deep learning to automatically segment and analyze abdominal aortic aneurysm from computed tomography angiography. Cardiovasc Eng Technol 2022; 13(4): 535-547.
  9. Caradu C, et al. Fully automatic volume segmentation of infrarenal abdominal aortic aneurysm computed tomography images with deep learning approaches versus physician controlled manual segmentation. J Vasc Surg 2021; 74(1): 246-256.
  10. López-Linares K, et al. 3D convolutional neural network for abdominal aortic aneurysm segmentation. arXiv Preprint. 2019. Source:   <https://arxiv.org/abs/1903.00879>.
  11. Lalys F, et al. Generic thrombus segmentation from pre-and post-operative CTA. Int J Comput Assist Radiol Surg 2017; 12(9): 1501-1510.
  12. Lareyre F, et al. A fully automated pipeline for mining abdominal aortic aneurysm using image segmentation. Sci Rep 2019; 9(1): 13750.
  13. Fedotova Y, et al. Automatically hemodynamic analysis of AAA from CT images based on deep learning and CFD approaches. J Phys Conf Ser 2021; 2119: 012069.
  14. Kloenne M, et al. Domain-specific cues improve robustness of deep learning-based segmentation of CT volumes. Sci Rep 2020; 10(1): 10712.
  15. Kurmukov A, et al. Challenges in building of deep learning models for glioblastoma segmentation: Evidence from clinical data. Stud Health Technol Inform 2021; 27(281): 298-302.
  16. Yushkevich PA, et al. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 2006; 31(3): 1116-1128.
  17. Qamar S, Ahmad P, Shen L. Dense encoder-decoder–based architecture for skin lesion segmentation. Cogn Comput 2021; 13(2): 583-594.
  18. Rundo L, et al. USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets. Neurocomputing 2019; 365: 31-43.
  19. Ibtehaz N, Rahman MS. MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw 2020; 121: 74-87.
  20. He K, et al. Deep residual learning for image recognition. Proc IEEE Conf on Computer Vision and Pattern Recognition 2016: 770-778.
  21. Paszke A, et al. Automatic differentiation in PyTorch. 31st Conf on Neural Information Processing Systems (NIPS 2017) 2017: 1-4.
  22. Odena A, Dumoulin V, Olah C. Deconvolution and checkerboard artifacts. Distill 2016; 1(10): e3.
  23. Roy AG, Navab N, Wachinger C. Recalibrating fully convolutional networks with spatial and channel “squeeze and excitation” blocks. IEEE Trans Med Imaging 2018; 38(2): 540-549.
  24. Iakubovskii P. Segmentation_models.pytorch. 2019. Source: <https://github.com/qubvel/segmentation_models.pytorch>.
  25. Loshchilov I, Hutter F. Fixing weight decay regularization in Adam. ICLR 2018 Conf Blind Submission. 2018. Source: <https://openreview.net/forum?id=rk6qdGgCZ>.
  26. Lin T-Y, et al. Focal loss for dense object detection. Proc IEEE Int Conf on Computer Vision 2017: 2980-2988.
  27. Cardoso MJ, et al, eds. Deep learning in medical image analysis and multimodal learning for clinical decision support. Cham: Springer International Publishing AG; 2017.
  28. Buslaev A, et al. Albumentations: fast and flexible image augmentations. Information 2020; 11(2): 125.
  29. Cardoso MJ, et al. MONAI: An open-source framework for deep learning in healthcare. arXiv Preprint. 2022. Source: <https://arxiv.org/abs/2211.02701>.
  30. Mucherino A, et al. K-nearest neighbor classification. In Book: Mucherino A, Papajorgji PJ, Pardalos PM, eds. Data mining in agriculture. Dordrecht: Springer Science+Business Media LLC; 2009: 83-106.
  31. Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 2015; 15(1): 29.

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