Ahmadi, M., Sharifi, A., Jafarian Fard, M., & Soleimani, N. (2023). Detection of brain lesion location in MRI images using convolutional neural network and robust PCA. International Journal of Neuroscience, 133(1), 55–66.
Alagarsamy, S., Kamatchi, K., Govindaraj, V., Zhang, Y., & Thiyagarajan, A. (2019). Multi-channeled MR brain image segmentation: A new automated approach combining BAT and clustering technique for better identification of heterogeneous tumors. Biocybernetics and Biomedical Engineering, 39(4), 1005–1035.
Alexiou, G. A., Goussia, A., Voulgaris, S., & Kyritsis, A. P. (2010). Management of meningiomas. Clinical Neurology and Neurosurgery, 112(3), 177–182.
Anita, J. N., & Kumaran, S. (2022). A Deep Learning Architecture for Meningioma Brain Tumor Detection and Segmentation. Journal of Cancer Prevention, 27(3), 192–198.
Article PubMed PubMed Central Google Scholar
Aswani, K., & Menaka, D. (2021). A dual autoencoder and singular value decomposition based feature optimization for the segmentation of brain tumor from MRI images. Bmc Medical Imaging, 21(1), 82. https://doi.org/10.1186/s12880-021-00614-3
Article CAS PubMed PubMed Central Google Scholar
Aswathy, S. U., & Abraham, A. (2021). Automated Detection and Classification of Meningioma Tumor from MR images using Sea Lion optimization and deep learning models. Axioms, 11(1), 15.
Balamurugan, T., & Gnanamanoharan, E. (2023). Brain tumor segmentation and classification using hybrid deep CNN with LuNetClassifier. Neural Comput & Applic, 35, 4739–4753.
Boaro, A., Kaczmarzyk, J. R., Kavouridis, V. K., Harary, M., Mammi, M., Dawood, H., Shea, A., Cho, E. Y., Juvekar, P., Noh, T., Rana, A., Ghosh, S., & Arnaout, O. (2022). Deep neural networks allow expert-level brain meningioma segmentation and present potential for improvement of clinical practice. Scientific Reports, 12(1), 15462.
Article CAS PubMed PubMed Central Google Scholar
Boelders, S. M., De Baene, W., Rutten, G., Gehring, K., & Ong, L. L. (2022). P18.08.B fully automatic meningioma segmentation using T1-weighted contrast-enhanced MR images only. Neuro-oncology (Charlottesville, Va.), 24(Supplement_2), ii95–ii96.
Borenstein, M., Hedges, L. V., Rothstein, H. R., Editors, M., & Borenstein (2009). (Hoboken, NJ, USA: Wiley), 77–86.
Borenstein, M., & Higgins, J. P. (2013). Meta-analysis and subgroups. Prevention Science, 14(2), 134–143. https://doi.org/10.1007/s11121-013-0377-7
Bouget, D., Eijgelaar, R. S., Pedersen, A., Kommers, I., Ardon, H., Barkhof, F., Bello, L., Berger, M. S., Nibali, M. C., Furtner, J., Fyllingen, E. H., Hervey-Jumper, S., Idema, A. J. S., Kiesel, B., Kloet, A., Mandonnet, E., Müller, D. M. J., Robe, P. A., Rossi, M., Sagberg, L. M., Sciortino, T., Van den Brink, W. A., Wagemakers, M., Widhalm, G., Witte, M. G., Zwinderman, A. H., Reinertsen, I., De Witt Hamer, P. C., & Solheim, O. (2021). Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task. Cancers (Basel). ;13(18):4674.
Bouget, D., Pedersen, A., Hosainey, S. A. M., Solheim, O., & Reinertsen, I. (2021). Meningioma Segmentation in T1-Weighted MRI leveraging global context and attention mechanisms. Front Radiol, 1, 711514.
Article PubMed PubMed Central Google Scholar
Bouget, D., Pedersen, A., Hosainey, S. A. M., Vanel, J., Solheim, O., & Reinertsen, I. (2021). Fast meningioma segmentation in T1-weighted magnetic resonance imaging volumes using a lightweight 3D deep learning architecture. J Med Imaging (Bellingham), 8(2), 024002.
Bouget, D., Pedersen, A., Jakola, A. S., Kavouridis, V., Emblem, K. E., Eijgelaar, R. S., Kommers, I., Ardon, H., Barkhof, F., Bello, L., Berger, M. S., Conti Nibali, M., Furtner, J., Hervey-Jumper, S., Idema, A. J. S., Kiesel, B., Kloet, A., Mandonnet, E., Müller, D. M. J., Robe, P. A., Rossi, M., Sciortino, T., Van den Brink, W. A., Wagemakers, M., Widhalm, G., Witte, M. G., Zwinderman, A. H., De Witt Hamer, P. C., Solheim, O., & Reinertsen, I. (2022). Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting. Front Neurol. ;13:932219.
Cekic, E., Pinar, E., Pinar, M., & Dagcinar, A. (2024). Deep learning-assisted segmentation and classification of brain tumor types on magnetic resonance and Surgical microscope images. World Neurosurg, 182, e196–e204.
Chen, C., Cheng, Y., Xu, J., Zhang, T., Shu, X., Huang, W., Hua, Y., Zhang, Y., Teng, Y., Zhang, L., & Xu, J. (2021). Automatic meningioma segmentation and grading prediction: A Hybrid Deep-Learning Method. J Pers Med, 11(8), 786.
Article PubMed PubMed Central Google Scholar
Chen, H., Li, S., Zhang, Y., Liu, L., Lv, X., Yi, Y., Ruan, G., Ke, C., & Feng, Y. (2022). Deep learning-based automatic segmentation of meningioma from multiparametric MRI for preoperative meningioma differentiation using radiomic features: A multicentre study. European Radiology, 32(10), 7248–7259.
Chen, C., Teng, Y., Tan, S., Wang, Z., Zhang, L., & Xu, J. (2023). Performance test of a well-trained model for Meningioma Segmentation in Health Care Centers: Secondary analysis based on four Retrospective Multicenter Data sets. Journal of Medical Internet Research, 25, e44119.
Article PubMed PubMed Central Google Scholar
Clark, V. E., Erson-Omay, E. Z., Serin, A., et al. (2013). Genomic analysis of non-NF2 meningiomas reveals mutations in TRAF7, KLF4, AKT1, and SMO. Science, 339(6123), 1077–1080.
Article CAS PubMed PubMed Central Google Scholar
Di Ieva, A., Russo, C., Liu, S., Jian, A., Bai, M. Y., Qian, Y., & Magnussen, J. S. (2021). Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: A heuristic approach in the clinical scenario. Neuroradiology, 63(8), 1253–1262.
Divya, S., Padma Suresh, L., & John, A. (2022). Enhanced deep-joint segmentation with deep learning networks of glioma tumor for multi-grade classification using MR images. Pattern Anal Applic, 25, 891–911.
Dong, Y., Wang, T., Ma, C., Li, Z., & Chellali, R. (2023). DE-UFormer: U-shaped dual encoder architectures for brain tumor segmentation. Physics in Medicine and Biology, 68(19), 195019.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2020). An image is worth 16x16 words. Transformers for Image Recognition at Scale. arXiv (Cornell University).
Dweik, M., & Ferretti, R. (2022). Integrating anisotropic filtering, level set methods and convolutional neural networks for fully automatic segmentation of brain tumors in magnetic resonance imaging. Neuroscience Informatics, 2(3), 100095.
Egger, M., Davey Smith, G., Schneider, M., & Minder, C. (1997). Bias in Meta-Analysis detected by a simple. Graphical Test BMJ, 315, 629–634. https://doi.org/10.1136/bmj.315.7109.629
Article CAS PubMed Google Scholar
Gab Allah, M., Sarhan, A. M., A., & Elshennawy, M., N (2023). Edge U-Net: Brain tumor segmentation using MRI based on deep U-Net model with boundary information. Expert Systems with Applications, 213, 118833.
Goldbrunner, R., Minniti, G., Preusser, M., et al. (2016). EANO guidelines for the diagnosis and treatment of meningiomas. The Lancet Oncology, 17(9), e383–e391.
Gryska, E., Björkman-Burtscher, I., Jakola, A. S., Dunås, T., Schneiderman, J., & Heckemann, R. A. (2022). Deep learning for automatic brain tumour segmentation on MRI: Evaluation of recommended reporting criteria via a reproduction and replication study. British Medical Journal Open, 12(7), e059000.
Gunasekara, S. R., Kaldera, H. N. T. K., & Dissanayake, M. B. (2021). A systematic approach for MRI brain tumor localization and segmentation using deep learning and active contouring. Journal of Healthcare Engineering (Print), 2021, 1–13.
Haq, E. U., Jianjun, H., Huarong, X., Li, K., & Weng, L. (2022). A Hybrid Approach based on deep CNN and Machine Learning classifiers for the Tumor segmentation and classification in Brain MRI. Computational and Mathematical Methods in Medicine, 2022, 6446680.
Article PubMed PubMed Central Google Scholar
Haq, A. U., Li, J. P., Agbley, B. L. Y., Khan, A., Khan, I., Uddin, M. I., & Khan, S. (2022). IIMFCBM: Intelligent Integrated Model for feature extraction and classification of brain tumors using MRI clinical Imaging Data in IoT-Healthcare. IEEE J Biomed Health Inform, 26(10), 5004–5012.
Harary, M., Boaro, A., Kavouridis, V., Kaczmarzyk, J., Mammi, M., Dawood, H., Ghosh, S., & Arnaout, O. (2020). Automated meningioma detection and segmentation using deep neural networks. Journal of Neurological Surgery Part B, Skull Base (Internet).
Hare Krishna Mishra, Manpreet Kaur. (2022). Multi Class Brain Tumor Segmentation Based on K-Means clustering technique. Neuroquantology Volume, 20, 17.
Hatamizadeh, A., Yang, D., Roth, H., & Xu, D. (2021). UNETR: Transformers for 3D Medical Image Segmentation. arXiv (Cornell University).
Havaei, M., Davy, A., Warde-Farley, D., et al. (2017). Brain tumor segmentation with deep neural networks. Medical Image Analysis, 35, 18–31.
Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in Meta-analyses. Bmj, 327, 557–560. https://doi.org/10.1136/bmj.327.7414.557
Article PubMed PubMed Central Google Scholar
Huang, H., Liu, P., & Liu, J. (2023). TAGU-Net: Transformer convolution hybrid-based U-Net with attention gate for atypical Meningioma Segmentation, in IEEE Access, 11, pp. 53207–53223.
Hwang, K., Park, J., Kwon, Y., Cho, S. J., Choi, B. S., Kim, J., Kim, E., Jang, J., Ahn, K., Kim, S., & Kim, C. (2022). Fully automated segmentation models of Supratentorial meningiomas assisted by inclusion of normal brain images. Journal of Imaging, 8(12), 327.
Article PubMed PubMed Central Google Scholar
Jun, Y., Park, Y. W., Shin, H., Shin, Y., Lee, J. R., Han, K., Ahn, S. S., Lim, S. M., Hwang, D., & Lee, S. K. (2023). Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning. European Radiology, 33(9), 6124–6133.
留言 (0)