2000 character limit reached
Spatiotemporal Disentanglement of Arteriovenous Malformations in Digital Subtraction Angiography (2402.09636v1)
Published 15 Feb 2024 in eess.IV and cs.CV
Abstract: Although Digital Subtraction Angiography (DSA) is the most important imaging for visualizing cerebrovascular anatomy, its interpretation by clinicians remains difficult. This is particularly true when treating arteriovenous malformations (AVMs), where entangled vasculature connecting arteries and veins needs to be carefully identified.The presented method aims to enhance DSA image series by highlighting critical information via automatic classification of vessels using a combination of two learning models: An unsupervised machine learning method based on Independent Component Analysis that decomposes the phases of flow and a convolutional neural network that automatically delineates the vessels in image space. The proposed method was tested on clinical DSA images series and demonstrated efficient differentiation between arteries and veins that provides a viable solution to enhance visualizations for clinical use.
- Frisken, S., Haouchine, N., Du, R., and Golby, A. J., “Using temporal and structural data to reconstruct 3d cerebral vasculature from a pair of 2d digital subtraction angiography sequences,” Computerized Medical Imaging and Graphics 99, 102076 (2022).
- Strother, C., Bender, F., Deuerling-Zheng, Y., Royalty, K., Pulfer, K., Baumgart, J., Zellerhoff, M., Aagaard-Kienitz, B., Niemann, D., and Lindstrom, M., “Parametric color coding of digital subtraction angiography,” American Journal of Neuroradiology 31(5), 919–924 (2010).
- Zhang, X., Chen, X., Li, C., and Zhang, F., “Color-coded imaging with adaptive multiscale spatial filtering,” in [Health Information Science: Third International Conference, HIS 2014, Shenzhen, China, April 22-23, 2014. Proceedings 3 ], 121–128, Springer (2014).
- Lee, H.-J., Hong, J.-S., Lin, C.-J., Kao, Y.-H., Chang, F.-C., Luo, C.-B., and Chu, W.-F., “Automatic flow analysis of digital subtraction angiography using independent component analysis in patients with carotid stenosis,” PloS one 12(9), e0185330 (2017).
- Huang, T.-C., Wu, T.-H., Lin, Y.-H., Guo, W.-Y., Huang, W.-C., and Lin, C.-J., “Quantitative flow measurement by digital subtraction angiography in cerebral carotid stenosis using optical flow method,” Journal of X-ray science and technology 21(2), 227–235 (2013).
- Momeni, S. and Pourghassem, H., “An automatic aneurysm extraction algorithm in fused brain digital subtraction angiography images,” Biocybernetics and Biomedical Engineering 35(4), 264–275 (2015).
- Podgoršak, A. R., Bhurwani, M. M., Rava, R. A., Chandra, A. R., and Ionita, C. N., “Use of a convolutional neural network for aneurysm identification in digital subtraction angiography,” in [Medical Imaging 2019: Computer-Aided Diagnosis ], 10950, 1106–1118, SPIE (2019).
- Su, R., Cornelissen, S. A., Van Der Sluijs, M., Van Es, A. C., Van Zwam, W. H., Dippel, D. W., Lycklama, G., Van Doormaal, P. J., Niessen, W. J., Van Der Lugt, A., et al., “autotici: Automatic brain tissue reperfusion scoring on 2d dsa images of acute ischemic stroke patients,” IEEE transactions on medical imaging 40(9), 2380–2391 (2021).
- Shi, K., Xiao, W., Wu, G., Xiao, Y., Lei, Y., Yu, J., and Gu, Y., “Temporal-spatial feature extraction of dsa video and its application in avm diagnosis,” Frontiers in Neurology 12, 655523 (2021).
- Meng, C., Sun, K., Guan, S., Wang, Q., Zong, R., and Liu, L., “Multiscale dense convolutional neural network for dsa cerebrovascular segmentation,” Neurocomputing 373, 123–134 (2020).
- Neumann, C., Tönnies, K., and Pohle-Fröhlich, R., “Angiounet - a convolutional neural network for vessel segmentation in cerebral dsa series,” Proc VISIGRAPP , 331–338 (01 2018).
- Hyvarinen, A., “Fast and robust fixed-point algorithms for independent component analysis,” IEEE transactions on Neural Networks 10(3), 626–634 (1999).
- Bell, A. J. and Sejnowski, T. J., “An information-maximization approach to blind separation and blind deconvolution,” Neural computation 7(6), 1129–1159 (1995).
- Haouchine, N., Juvekar, P., Xiong, X., Luo, J., Kapur, T., Du, R., Golby, A., and Frisken, S., “Estimation of high framerate digital subtraction angiography sequences at low radiation dose,” in [Medical Image Computing and Computer-Assisted Intervention - MICCAI 2021 ], 171–180 (2021).
- Ronneberger, O., P.Fischer, and Brox, T., “U-net: Convolutional networks for biomedical image segmentation,” in [Medical Image Computing and Computer-Assisted Intervention (MICCAI) ], LNCS 9351, 234–241, Springer (2015).
- Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C., “Mobilenetv2: Inverted residuals and linear bottlenecks,” in [Proceedings of the IEEE conference on computer vision and pattern recognition ], 4510–4520 (2018).