Abstract
Subarachnoid hemorrhage, commonly caused by the rupture of cerebral aneurysms, is a life-threatening condition with high mortality and morbidity. With a death rate of roughly 40%, it is highly desirable to detect aneurysms early and decide about the appropriate rupture prevention strategy. Rotational X-ray angiography is a non-invasive imaging modality and enables diagnostics to detect cerebral aneurysms at an early stage.
We propose a variation of the 3D U-Net architecture for the detection and localization of these cerebral aneurysms. This model is enhanced with a knowledge-based postprocessing strategy to minimize the false-positive detections per case. Our suggested method shows similar sensitivity statistics compared to state-of-the-art solutions, with a drastically reduced false-positive rate per patient. The described solution is almost entirely accurate on structures larger than 5 mm in diameter but shows difficulties with smaller aneurysms. We show an F2-score of 0.84 and a false-positive rate of 0.41 on a private test set.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bhidayasiri, R., et al.: Neurological differential diagnosis: a prioritized approach (2005)
Teunissen, L.L., et al.: Risk factors for subarachnoid hemorrhage (1996)
Park, A., et al.: Deep learning-assisted diagnosis of cerebral aneurysms using the HeadXNet model. JAMA Netw. Open 2, e195600–e195600 (2019)
Faron, A., et al.: Performance of a deep-learning neural network to detect intracranial aneurysms from 3D TOF-MRA compared to human readers. Clin. Neuroradiol. 30, 591–598 (2019)
Hirai, T., et al.: Intracranial aneurysms at MR angiography: effect of computer-aided diagnosis on radiologists’ detection performance. Radiology 237, 605–610 (2005)
Arimura, H., et al.: Automated computerized scheme for detection of unruptured intracranial aneurysms in three-dimensional magnetic resonance angiography. Acad. Radiol. 11, 1093–1104 (2004)
Lauric, A., et al.: Automated detection of intracranial aneurysms based on parent vessel 3D analysis. Med. Image Anal. 14(2), 149–159 (2010)
Yang, X., et al.: Computer-aided detection of intracranial aneurysms in MR angiography. J. Digit. Imaging 24(1), 86–95 (2011)
Hentschke, C., et al.: Detection of cerebral aneurysms in MRA, CTA and 3D-RA data sets (2012)
Hentschke, C., et al.: A new feature for automatic aneurysm detection (2012)
Chen, S.-P., et al.: Evaluation of imaging diagnosis and assessment value of three-dimensional digital angiography for intracranial aneurysms (2012)
Koc, K., et al.: Detection and evaluation of intracranial aneurysms with 3D-CT angiography and compatibility of simulation view with surgical observation (2014)
Sulayman, N., et al.: Semi-automatic detection and segmentation algorithm of saccular aneurysms in 2D cerebral DSA images. Egypt. J. Radiol. Nucl. Med. 47(3), 859–865 (2016)
Nakao, T., et al.: Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography. J. Magn. Reson. Imaging 47(4), 948–953 (2018)
Rahmany, I., et al.: A fully automatic based deep learning approach for aneurysm detection in DSA images (2018)
Ueda, D., et al.: Deep learning for MR angiography: automated detection of cerebral aneurysms. Radiology 290(1), 187–194 (2019)
Joo, B., et al.: A deep learning algorithm may automate intracranial aneurysm detection on MR angiography with high diagnostic performance. Eur. Radiol. 30, 5785–5793 (2020)
Stember, J., et al.: Convolutional neural networks for the detection and measurement of cerebral aneurysms on magnetic resonance angiography. J. Digit. Imaging 32(5), 808–815 (2019)
Chen, G., et al.: Automated computer-assisted detection system for cerebral aneurysms in time-of-flight magnetic resonance angiography using fully convolutional network. BioMed. Eng. OnLine 19, 1–10 (2020)
Jin, H., et al.: Fully automated intracranial aneurysm detection and segmentation from digital subtraction angiography series using an end-to-end spatiotemporal deep neural network. J. NeuroInterventional Surg. 12(10), 1023–1027 (2020)
Sichtermann, T., et al.: Deep learning-based detection of intracranial aneurysms in 3D TOF-MRA. Am. J. Neuroradiol. 40(1), 25–32 (2019)
Patel, T., et al.: Multi-resolution CNN for brain vessel segmentation from cerebrovascular images of intracranial aneurysm: a comparison of U-Net and DeepMedic (2020)
Zhang, Y., et al.: DDNet: a novel network for cerebral artery segmentation from MRA images (2019)
Dai, X., et al.: Deep learning for automated cerebral aneurysm detection on computed tomography images. Int. J. Comput. Assist. Radiol. Surg. 15, 715–723 (2020)
Zeng, Y., et al.: Automatic diagnosis based on spatial information fusion feature for intracranial aneurysm. IEEE Trans. Med. Imaging 39(5), 1448–1458 (2020)
Zhou, M., Wang, X., Wu, Z., Pozo, J.M., Frangi, A.F.: Intracranial aneurysm detection from 3D vascular mesh models with ensemble deep learning. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 243–252. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_27
Yang, X., et al.: Surface-based 3D deep learning framework for segmentation of intracranial aneurysms from TOF-MRA images (2020)
Duan, H., et al.: Automatic detection on intracranial aneurysm from digital subtraction angiography with cascade convolutional neural networks. Biomed. Eng. Online 18(1), 1–18 (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Szegedy, C., et al.: Rethinking the inception architecture for computer vision (2015)
Ioffe, S., et al.: Batch normalization: accelerating deep network training by reducing internal covariate shift (2015)
Tompson, J., et al.: Efficient object localization using convolutional networks (2015)
Ng, A.Y., et al.: On spectral clustering: analysis and an algorithm. Adv. Neural Inf. Process. Syst. 2, 849–856 (2001)
Sulayman, N., et al.: Semi-automatic detection and segmentation algorithm of saccular aneurysms in 2D cerebral DSA images. Egypt. J. Radiol. Nuclear Med. 47(3), 859–865 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Ivantsits, M., Kuhnigk, JM., Huellebrand, M., Kuehne, T., Hennemuth, A. (2021). Deep Learning-Based 3D U-Net Cerebral Aneurysm Detection. In: Hennemuth, A., Goubergrits, L., Ivantsits, M., Kuhnigk, JM. (eds) Cerebral Aneurysm Detection and Analysis. CADA 2020. Lecture Notes in Computer Science(), vol 12643. Springer, Cham. https://doi.org/10.1007/978-3-030-72862-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-72862-5_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72861-8
Online ISBN: 978-3-030-72862-5
eBook Packages: Computer ScienceComputer Science (R0)