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A 3D U-Net-Based Approach for Intracranial Aneurysm Detection

Published: 22 May 2023 Publication History

Abstract

Intracranial aneurysm refers to a neoplastic protrusion of the arterial wall caused by a localized abnormal enlargement of the cerebral artery lumen. In clinical practice, patients in the early stage of onset generally have no obvious symptoms, which is very easy to miss diagnosis. In medicine, methods such as MRA, CTA and DSA can be used to display the images of blood vessels. Among them, magnetic resonance angiography (MRA) has the advantages of low cost and small damage to the human body. Which can display the images of blood vessels in the brain. The data set used herein was based on images provided by a three-dimensional time-of-flight magnetic resonance angiography system. The main contributions of this paper are as follows: (1) We improved a classic 3D U-Net model with the combination of attention gate, residual connection, and the changes of size. Which achieved automatic segmentation of aneurysms in MRA. In the detection of aneurysms with mean diameters of 6.10mm and 7.69mm, the sensitivity was 83.4% and 86.4% respectively. (2) On the basis of this sensitivity, we achieved a low false positive rate which was 0.36 FPs/case and 0.34 FPs/case respectively.
CCS CONCEPTS • Computing methodologies∼Computer graphics∼Image manipulation∼Image processing

References

[1]
Vlak M. H., Algra A., Brandenburg R. and Rinkel, G. J. 2011. Prevalence of unruptured intracranial aneurysms, with emphasis on sex, age, comorbidity, country, and time period: a systematic review and meta-analysis. The Lancet Neurology, 10(7), 626-636
[2]
Thien A., See A. A. Q., Ang S. Y. L., Primalani N. K., Lim M. J. R., Ng Y. P. and King N. K. K. (2017). Prevalence of asymptomatic unruptured intracranial aneurysms in a Southeast Asian population. World neurosurgery, 97, 326-332
[3]
Jeon T. Y., Jeon P. and Kim K. H. 2011. Prevalence of unruptured intracranial aneurysm on MR angiography. Korean journal of radiology, 12(5), 547-553
[4]
Ropper A. H. and Zervas N. T. 1984. Outcome 1 year after SAH from cerebral aneurysm: management morbidity, mortality, and functional status in 112 consecutive good-risk patients. Journal of neurosurgery, 60(5), 909-915
[5]
Juvela S., Porras M. and Poussa K. 2000. Natural history of unruptured intracranial aneurysms: probability of and risk factors for aneurysm rupture. Journal of neurosurgery, 93(3), 379-387
[6]
Ingall T., Asplund K., Mähönen M. and Bonita R. 2000. A multinational comparison of subarachnoid hemorrhage epidemiology in the WHO MONICA stroke study. Stroke, 31(5), 1054-1061
[7]
El Hamdaoui H., Maaroufi M., Alami B., Chaoui N. E. and Boujraf S. 2017. Computer-aided diagnosis systems for detecting intracranial aneurysms using 3D angiographic data sets. In 2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (pp. 1-5). IEEE
[8]
Lekadir K, Galimzianova A, Betriu A, Vila, M. D. M. and Napel S. 2016. A convolutional neural network for automatic characterization of plaque composition in carotid ultrasound. IEEE Journal of Biomedical & Health Informatics, 21(99), 48
[9]
Hanaoka S., Nomura Y., Takenaga T., Murata M., Nakao T., Miki S. and Shimizu A. 2019. HoTPiG: a novel graph-based 3-D image feature set and its applications to computer-assisted detection of cerebral aneurysms and lung nodules. International journal of computer assisted radiology and surgery, 14(12), 2095-2107
[10]
Yu L., Cheng J. Z., Dou Q., Yang X., Chen H., Qin J., ... and Heng P A. 2017. Automatic 3D cardiovascular MR segmentation with densely-connected volumetric convnets. In International conference on medical image computing and computer-assisted intervention (pp. 287-295). Springer, Cham
[11]
Ueda D., Yamamoto A., Nishimori M., Shimono T., Doishita S., Shimazaki A. and Miki Y. 2019. Deep learning for MR angiography: automated detection of cerebral aneurysms. Radiology, 290(1), 187-194
[12]
Yuki Enokiya, Yutaro Iwamoto, Yen-Wei Chen, and Xian-Hua Han, "Automatic Liver Segmentation Using U-Net with Wasserstein GANs," Journal of Image and Graphics, Vol. 6, No. 2, pp. 152-159, December 2018.
[13]
Sichtermann T., Faron A., Sijben R., Teichert N., Freiherr J. and Wiesmann M. 2019. Deep learning–based detection of intracranial aneurysms in 3D TOF-MRA. American Journal of Neuroradiology, 40(1), 25-32
[14]
Joo B., Ahn S. S., Yoon P. H., Bae S., Sohn B., Lee Y. E. and Lee S. K. 2020. A deep learning algorithm may automate intracranial aneurysm detection on MR angiography with high diagnostic performance. European Radiology, 30(11), 5785-5793
[15]
Isensee F., Jäger P. F., Kohl S. A., Petersen J. and Maier-Hein K. H. 2019. Automated design of deep learning methods for biomedical image segmentation. arXiv preprint arXiv:1904.08128
[16]
Çiçek Ö., Abdulkadir A., Lienkamp S. S., Brox T. and Ronneberger O. 2016. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In International conference on medical image computing and computer-assisted intervention (pp. 424-432). Springer, Cham
[17]
Xiao X., Lian S., Luo Z. and Li S. 2018. Weighted res-unet for high-quality retina vessel segmentation. In 2018 9th international conference on information technology in medicine and education (ITME) (pp. 327-331). IEEE
[18]
Oktay O., Schlemper J., Folgoc L. L., Lee M., Heinrich M., Misawa K. and Rueckert D. 2018. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999
[19]
B.Joo,S.S.Ahn,P.H.Yoon,S.Bae,B.Sohn,Y.E.Lee and Lixiang, huang. 2021. Deep learning algorithm can automatically detect intracranial aneurysms on MR angiography with high diagnostic performance. International Journal of Medical Radiology (01),115.
[20]
Jiehua Yang. 2021. Research on detection algorithm of cerebral aneurysm in CTA images based on 3D convolution neural network. Master Thesis, Qingdao University.

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  • (2024) Staged cluster transformers for intracranial aneurysms segmentation from structure fused 3D MRA International Journal of Imaging Systems and Technology10.1002/ima.2303934:2Online publication date: 14-Feb-2024

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ICCPR '22: Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition
November 2022
683 pages
ISBN:9781450397056
DOI:10.1145/3581807
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 22 May 2023

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Author Tags

  1. 3D U-Net
  2. Intracranial aneurysm detection
  3. Machine learning
  4. Neural networks

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  • (2024) Staged cluster transformers for intracranial aneurysms segmentation from structure fused 3D MRA International Journal of Imaging Systems and Technology10.1002/ima.2303934:2Online publication date: 14-Feb-2024

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