Morphology and Texture-Guided Deep Neural Network for Intracranial Aneurysm Segmentation in 3D TOF-MRA

Anima, V., & Madhu, S. (2024). On the automated unruptured intracranial aneurysm segmentation from TOF-MRA using deep learning techniques. Ieee Access : Practical Innovations, Open Solutions.

Claux, F., Baudouin, M., Bogey, C., & Rouchaud, A. (2023). Dense, deep learning-based intracranial aneurysm detection on TOF MRI using two-stage regularized U-Net. Journal of Neuroradiology, 50(1), 9–15.

Article  PubMed  Google Scholar 

Di Noto, T., Marie, G., Tourbier, S., Alemán-Gómez, Y., & Richiardi, J. (2023). Towards automated brain aneurysm detection in TOF-MRA: Open data, weak labels, and anatomical knowledge. Neuroinformatics, 21(1), 21–34.

Article  PubMed  Google Scholar 

El Jurdi, R., Petitjean, C., Honeine, P., Cheplygina, V., & Abdallah, F. (2021). High-level prior-based loss functions for medical image segmentation: A survey. Computer Vision and Image Understanding, 210,

Article  Google Scholar 

Etminan, N., & Rinkel, G. J. (2016). Unruptured intracranial aneurysms: Development, rupture and preventive management. Nature Reviews Neurology, 12(12), 699–713.

Article  PubMed  Google Scholar 

Ham, S., Seo, J., Yun, J., Bae, Y., Kim, T., Sunwoo, L., & Kim, N. (2023). Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA. Scientific Reports, 13(1), 12018.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Haskins, G., Kruger, U., & Yan, P. (2020). Deep learning in medical image registration: A survey. Machine Vision and Applications, 31, 1–18.

Article  Google Scholar 

Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2018). nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203–211.

Article  Google Scholar 

Işın, A., Direkoğlu, C., & Şah, M. (2016). Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Computer Science, 102, 317–324.

Article  Google Scholar 

Kervadec, H., Bouchtiba, J., Desrosiers, C., Granger, E., Dolz, J., & Ayed, I. B. (2018). Boundary loss for highly unbalanced segmentation. arXiv Preprint arXiv:181207032.

Lin, T., Goyal, P., Girshick, R., & Dollr, P. (2017). Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV). 2999–3007.

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., Van Der Laak, J. A., Van Ginneken, B., & S´anchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88.

Article  PubMed  Google Scholar 

Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., & Martel, A. L. (2021). Loss odyssey in medical image segmentation. Medical Image Analysis, 71,

Article  PubMed  Google Scholar 

Milletari, F., Navab, N., & Ahmadi, S. (2016). V-net: Fully convolutional neural networks for volumetric medical image segmentation. In Fourth International Conference on 3D Vision (3DV).

Mirikharaji, Z., & Hamarneh, G. (2018). Star shape prior in fully convolutional networks for skin lesion segmentation. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2018, pp. 737–745.

Nichyporuk, B., Szeto, J., Arnold, D. L., & Arbel, T. (2021). Optimizing operating points for high performance lesion detection and segmentation using lesion size reweighting. ArXiv. /abs/2107.12978.

Patel, T. R., Patel, A., Veeturi, S. S., Shah, M., Waqas, M., Monteiro, A., & Tutino, V. M. (2023). Evaluating a 3D deep learning pipeline for cerebral vessel and intracranial aneurysm segmentation from computed tomography angiography–digital subtraction angiography image pairs. Neurosurgical Focus, 54(6), E13.

Article  PubMed  Google Scholar 

Qu, J., Niu, J., Li, Y., Chen, T., Peng, F., Xia, J., & Li, C. (2024). A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images. European Radiology, 34(5), 2838–2848.

Article  PubMed  Google Scholar 

Rachmadi, M. F., Byra, M., & Skibbe, H. (2024). A new family of instance-level loss functions for improving instance-level segmentation and detection of white matter hyperintensities in routine clinical brain MRI. Computers in Biology and Medicine, 174,

Article  PubMed  Google Scholar 

Rachmadi, M. F., Poon, C., & Skibbe, H. (2023). Improving segmentation of objects with varying sizes in Biomedical images using instance-wise and Center-of-Instance segmentation loss function. ArXiv./abs/2304.06229.

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (Eds.) Medical Image Computing and Computer-Assisted Intervention, pp. 234–241.

Salehi, S. S. M., Erdogmus, D., & Gholipour, A. (2019). Tversky loss function for image segmentation using 3d fully convolutional deep networks. In International Workshop on Machine Learning in Medical Imaging, pp. 379–387.

Shao, D., Lu, X., & Liu, X. (2022). 3D intracranial aneurysm classification and segmentation via unsupervised dual-branch learning. IEEE Journal of Biomedical and Health Informatics, 27(4), 1770–1779.

Article  Google Scholar 

Sherlock, M., Agha, A., & Tompson, C. J. (2006). Aneurysmal subarachnoid hemorrhage. New England Journal of Medicine, 354(16), 1755–1757.

Article  CAS  PubMed  Google Scholar 

Shirokikh, B., Shevtsov, A., Kurmukov, A., Dalechina, A., Krivov, E., Kostjuchenko, V., & Belyaev, M. (2020). Universal loss reweighting to balance lesion size inequality in 3D medical image segmentation. MICCAI, pp. 523–532.

Song, Y., Teoh, J., Choi, K., & Qin, J. (2023). Dynamic loss weighting for multiorgan segmentation in medical images. IEEE transactions on neural networks and learning systems. https://doi.org/10.1109/TNNLS.2023.3243241

Sudre, C., Li, W., Vercauteren, T., Ourselin, S., & Cardoso, M. (2017). Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 240–248.

Timmins, K. M., Van der Schaaf, I. C., Bennink, E., Ruigrok, Y. M., An, X., Baumgartner, M., & Kuijf, H. J. (2021). Comparing methods of detecting and segmenting unruptured intracranial aneurysms on TOF-MRAS: The ADAM challenge. Neuroimage, 238,

Article  PubMed  Google Scholar 

Vlak, M. H., Algra, A., Brandenburg, R., & 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.

Article  PubMed  Google Scholar 

Yu, B., Wang, Y., Wang, L., Shen, D., & Zhou, L. (2020). Medical image synthesis via deep learning. Deep Learning in Medical Image Analysis: Challenges and Applications, Advances in Experimental Medicine and Biology, 2020(1213), 23–44.

Article  Google Scholar 

Yuan, W., Peng, Y., Guo, Y., Ren, Y., & Xue, Q. (2022). DCAU-Net: Dense convolutional attention U-Net for segmentation of intracranial aneurysm images. Visual Computing for Industry Biomedicine and art, 5(1), 1–18.

Google Scholar 

Zhu, G., Luo, X., Yang, T., Cai, L., Yeo, J., Yan, G., & Yang, J. (2022). Deep learning-based recognition and segmentation of intracranial aneurysms under small sample size. Frontiers in Physiology, 13, 1084202.

Article  PubMed  PubMed Central  Google Scholar 

留言 (0)

沒有登入
gif