Automatic segmentation of vestibular schwannomas from T1-weighted MRI with a deep neural network
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[1] M. Staring,et al. Fully Automated 3D Vestibular Schwannoma Segmentation with and without Gadolinium-based Contrast Material: A Multicenter, Multivendor Study. , 2022, Radiology. Artificial intelligence.
[2] S. Ourselin,et al. Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm , 2021, Scientific Data.
[3] Eli Gibson,et al. Evaluating deep learning methods in detecting and segmenting different sizes of brain metastases on 3D post-contrast T1-weighted images , 2021, Journal of medical imaging.
[4] J. Cheng,et al. Randomized multi-reader evaluation of automated detection and segmentation of brain tumors in stereotactic radiosurgery with deep neural networks , 2021, Neuro-oncology.
[5] Chia-Feng Lu,et al. Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery , 2021, Scientific Reports.
[6] J. Flickinger,et al. Stereotactic radiosurgery as the first-line treatment for intracanalicular vestibular schwannomas. , 2020, Journal of neurosurgery.
[7] M. Shamim,et al. Steriotactic Radiosurgery for Vestibular Schwannomas. , 2020, JPMA. The Journal of the Pakistan Medical Association.
[8] V. Visser-Vandewalle,et al. Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data , 2020, Radiation Oncology.
[9] Di Zhao,et al. A review of the application of deep learning in medical image classification and segmentation , 2020, Annals of translational medicine.
[10] Lei Xing,et al. Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications , 2019, Medical physics.
[11] Sebastien Ourselin,et al. An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI. , 2019, Journal of neurosurgery.
[12] Sebastien Ourselin,et al. Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss , 2019, MICCAI.
[13] Fei Wang,et al. Deep learning for healthcare: review, opportunities and challenges , 2018, Briefings Bioinform..
[14] Tilak Das,et al. A comparison of semi-automated volumetric vs linear measurement of small vestibular schwannomas , 2018, European Archives of Oto-Rhino-Laryngology.
[15] D. Kondziolka,et al. The relationship of dose to nerve volume in predicting pain recurrence after stereotactic radiosurgery in trigeminal neuralgia. , 2017, Journal of neurosurgery.
[16] Seyed-Ahmad Ahmadi,et al. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).
[17] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] A. Tsimpas,et al. Analysis of vestibular schwannoma size: A literature review on consistency with measurement techniques , 2015, Clinical Neurology and Neurosurgery.
[19] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[20] Zhuowen Tu,et al. Deeply-Supervised Nets , 2014, AISTATS.
[21] A. Friedman,et al. Vestibular schwannomas in the modern era: epidemiology, treatment trends, and disparities in management. , 2013, Journal of Neurosurgery.
[22] T. Wentzel‐Larsen,et al. Analysis of vestibular schwannoma size in multiple dimensions: a comparative cohort study of different measurement techniques , 2010, Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery.
[23] B. McCarthy,et al. Temporal trends in incidence of primary brain tumors in the United States, 1985-1999. , 2006, Neuro-oncology.
[24] L. D. Lunsford. Vestibular schwannomas. , 2004, Neuro-Chirurgie.
[25] J. Kanzaki,et al. New and modified reporting systems from the consensus meeting on systems for reporting results in vestibular schwannoma. , 2003, Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology.