Hallpike, C. S. & Cairns, H. W. B. Observations of the pathology of Menie`re’s syndrome. Proc. R Soc. Med. 31, 1317–1336 (1938).PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Yamakawa, K. U¨ ber die pathologische Vera¨nderung beieinem M_enie`re-Kranken. Proceedings of 42nd Annual Meeting Oto-Rhino-Laryngol Soc Japan. J. Otolaryngol. Soc. Jpn. 4, 2310–2. (1938).Kimura, R. S. & Schuknecht, H. F. Membranous hydrops in the inner ear of the guinea pig after obliteration of the endolymphatic sac. Pract. Otorhinolaryngol. 27, 343–354 (1965).
Google ScholarÂ
Kimura, R. S. Experimental blockage of the endolymphatic duct and sac and its effect on the inner ear of the guinea pig. Ann. Otol Rhinol Laryngol. 76, 664–687 (1967).ArticleÂ
PubMedÂ
Google ScholarÂ
Kimura, R. S. Experimental pathogenesis of hydrops. Arch. Otorhinolaryngol. 212, 263–275 (1976).ArticleÂ
PubMedÂ
Google ScholarÂ
Kiang, N. Y. S. An auditory physiologist’s view of Ménière’s syndrome. In Second International Symposium on Ménière’s disease (ed. Nadol, J. B. Jr)13–24. (Kugler & Ghedini, Amsterdam 1989).Schuknecht, H. F. Pathology of the Ear. 2nd edn. (Lea & Febiger, Philadelphia, 1993).Merchant, S. N., Rauch, S. D. & Nadol, J. B. Meniere’s disease. Eur. Arch. Otorhinolaryngol. 252, 63–75 (1995).ArticleÂ
PubMedÂ
Google ScholarÂ
Nadol, J. B. Jr Pathogenesis of Meniere’s syndrome. In Ménière’s Disease, (ed. Harris, J. P.) 73–79 (The Hague, The Netherlands: Kugler, 1999).Nakashima, T. et al. Grading of endolymphatic hydrops using magnetic resonance imaging. Acta Otolaryngol. 129 (sup560), 5–8 (2009).ArticleÂ
Google ScholarÂ
Vaidyanathan, A. et al. Deep learning for the fully automated segmentation of the inner ear on MRI. Sci. Rep. 11 (1), 1–14 (2021).ArticleÂ
Google ScholarÂ
Hussain, R., Lalande, A., Girum, K. B., Guigou, C., Grayeli, B. & A Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network. Sci. Rep. 11 (1), 1–10 (2021).ArticleÂ
Google ScholarÂ
Zhu, S., Gao, W., Zhang, Y., Zheng, J., Liu, Z. & Yuan, G. 3D automatic MRI level set segmentation of inner ear based on statistical shape models prior. In 2017 10th International Congress on Image and Signal Processing, Biomedical Engineering and Informatics (CISP-BMEI) 1–6 (IEEE, 2017).Ahmadi, S. A., Raiser, T. M., Rühl, R. M., Flanagin, V. L. & Zu Eulenburg, P. IE-Map: a novel in-vivo atlas and template of the human inner ear. Sci. Rep. 11 (1), 1–16 (2021).ArticleÂ
Google ScholarÂ
Kirsch, V., Nejatbakhshesfahani, F., Ahmadi, S. A., Dieterich, M. & Ertl-Wagner, B. A probabilistic atlas of the human inner ear’s bony labyrinth enables reliable atlas-based segmentation of the total fluid space. J. Neurol. 266 (1), 52–61 (2019).ArticleÂ
PubMedÂ
Google ScholarÂ
Powell, K. A. et al. Atlas-based segmentation of temporal bone anatomy. Int. J. Comput. Assist. Radiol. Surg. 12 (11), 1937–1944 (2017).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Meng, J., Li, S., Zhang, F., Li, Q. & Qin, Z. Cochlear size and shape variability and implications in cochlear implantation surgery. Otol. Neurotol. 37(9), 1307–1313 (2016).ArticleÂ
PubMedÂ
Google ScholarÂ
Kendi, T. K., Arikan, O. K. & Koc, C. Volume of components of labyrinth: magnetic resonance imaging study. OtolNeurotol. 26 (4), 778–781 (2005).
Google ScholarÂ
Wang, R. et al. Medical image segmentation using deep learning: a survey. IET Image Proc. 16 (5), 1243–1267 (2022).ArticleÂ
Google ScholarÂ
Niyas, S., Pawan, S. J., Kumar, M. A. & Rajan, J. Medical image segmentation with 3D convolutional neural networks: a survey. Neurocomputing. 493, 397–413 (2022).ArticleÂ
Google ScholarÂ
Shen, D., Wu, G. & Suk, H. I. Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221 (2017).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Liu, X., Song, L., Liu, S. & Zhang, Y. A review of deep-learning-based medical image segmentation methods. Sustainability. 13 (3), 1224 (2021).ArticleÂ
Google ScholarÂ
Litjens, G. et al. A survey on deep learning in medical image analysis. Med. Image. Anal. 42, 60–88 (2017).ArticleÂ
PubMedÂ
Google ScholarÂ
Gürkov, R. et al. MR volumetric assessment of endolymphatic hydrops. Eur. Radiol. 25 (2), 585–595 (2015).ArticleÂ
PubMedÂ
Google ScholarÂ
Neves, C. A., Tran, E. D., Kessler, I. M. & Blevins, N. H. Fully automated preoperative segmentation of temporal bone structures from clinical CT scans. Sci. Rep. 11 (1), 1–11 (2021).ArticleÂ
Google ScholarÂ
Noble, J. H., Labadie, R. F., Majdani, O. & Dawant, B. M. Automatic segmentation of intracochlear anatomy in conventional CT. IEEE Trans. Biomed. Eng. 58 (9), 2625–2632 (2011).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Iyaniwura, J. E., Elfarnawany, M., Ladak, H. M. & Agrawal, S. K. An automated A-value measurement tool for accurate cochlear duct length estimation. J. Otolaryngology-Head Neck Surg. 47 (1), 1–8 (2018).ArticleÂ
Google ScholarÂ
Liu, T., Xu, Y., An, Y. & Ge, H. Intelligent segmentation algorithm for diagnosis of Meniere’s disease in the inner auditory canal using MRI images with three-dimensional level set. Contrast Media Mol. Imaging 2021 (2021).Heutink, F., Koch, V., Verbist, B., van der Woude, W. J., Mylanus, E., Huinck, W.,… & Caballo, M. Multi-scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images. Comput. Methods Programs Biomed. 191, 105387 (2020).Elfarnawany, M. et al. Micro-CT versus synchrotron radiation phase contrast imaging of human cochlea. J. Microsc. 265 (3), 349–357 (2017).ArticleÂ
PubMedÂ
Google ScholarÂ
Franz, D., Hofer, M., Pfeifle, M., Pirlich, M., Stamminger, M. & Wittenberg, T. Wizard-based segmentation for cochlear implant planning. In Bildverarbeitung für die Medizin 2014 258–263 (Springer, Berlin, Heidelberg, 2014).Naganawa, S. et al. MR imaging of endolymphatic hydrops: utility of iHYDROPS-Mi2 combined with deep learning reconstruction denoising. Magn. Reson. Med. Sci. 20 (3), 272–279 (2021).ArticleÂ
PubMedÂ
Google ScholarÂ
Cho, Y. S., Cho, K., Park, C. J., Chung, M. J., Kim, J. H., Kim, K.,… & Chung, W. H. Automated measurement of hydrops ratio from MRI in patients with Ménière’s disease using CNN-based segmentation. Sci. Rep. 10 (1), 1–10 (2020) Park, C. J., Cho, Y. S., Chung, M. J., Kim, Y. K., Kim, H. J., Kim, K.,… & Cho, B. H. A Fully automated analytic system for measuring endolymphatic hydrops ratios in patients With Ménière Disease via Magnetic Resonance Imaging: deep learning model development study. J. Med. Internet Res. 23 (9), e29678 (2021). Iida, T., Teranishi, M., Yoshida, T., Otake, H., Sone, M., Kato, M.,… & Nakashima, T. Magnetic resonance imaging of the inner ear after both intratympanic and intravenous gadolinium injections. Acta Oto-Laryngol. 133 (5), 434–438 (2013). Ronneberger, O., Fischer, P. & Brox, T. U-net: convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention 234–241 (Springer, Cham, 2015).Nogovitsyn, N., Souza, R., Muller, M., Srajer, A., Hassel, S., Arnott, S. R.,… & MacQueen, G. M. Testing a deep convolutional neural network for automated hippocampus segmentation in a longitudinal sample of healthy participants. NeuroImage 197, 589–597 (2019). Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T. & Ronneberger, O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In International Conference on Medical Image Computing and Computer-Assisted Intervention 424–432 (Springer, Cham, 2016).Loshchilov, I. & Hutter, F. Decoupled weight decay regularization. ICLR 2019 (2017).Abraham, N. & Khan, N. M. A novel focal tversky loss function with improved attention u-net for lesion segmentation. In 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019) pp. 683–687 (IEEE, 2019).Lin, T. Y., Goyal, P., Girshick, R., He, K. & Dollár, P. Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42, 2980–2991 (2018).Salehi, S. S. M., Erdogmus, D. & Gholipour, A. Tversky loss function for image segmentation using 3D fully convolutional deep networks. In International Workshop on Machine Learning in Medical Imaging 379–387 (Springer, Cham, 2017).Naganawa, S., Yamazaki, M., Kawai, H., Bokura, K., Sone, M. & Nakashima, T. Imaging of endolymphatic and perilymphatic fluid after intravenous administration of single-dose gadodiamide. Magn. Reson. Med. Sci. 11, 145–150 (2012).Szklo, M. & Nieto, F. J. Epidemiology: Beyond the Basics (Jones & Bartlett, 2014).Bland, J. M. & Altman, D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 327 (8476), 307–310 (1986).ArticleÂ
Google ScholarÂ
Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 7132–7141 (2018).Oktay, O. et al. Attention U-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018).Nodirov, J., Abdusalomov, A. B. & Whangbo, T. K. Attention 3D U-Net with multiple skip connections for segmentation of brain tumor images. Sensors. 22 (17), 6501 (2022).ArticleÂ
ADSÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Futrega, M., Milesi, A., Marcinkiewicz, M. & Ribalta, P.. Optimized U-Net for brain tumor segmentation. In International MICCAI Brainlesion Workshop 15–29 (Springer International Publishing, Cham, 2021).Hatamizadeh, A. et al. Swin unetr: swin transformers for semantic segmentation of brain tumors in mri images. In International MICCAI Brainlesion Workshop 272–284 (Springer International Publishing, Cham, 2021).Gao, Y. et al. A data-scalable transformer for medical image segmentation: architecture, model efficiency, and benchmark. arXiv preprint arXiv:2203.00131. (2022).Amit, T., Shaharbany, T., Nachmani, E. & Wolf, L. Segdiff: Image segmentation with diffusion probabilistic models. arXiv preprint arXiv:2112.00390. (2021).Wu, J., Fu, R., Fang, H., Zhang, Y. & Xu, Y. Medsegdiff-v2: Diffusion based medical image segmentation with transformer. arXiv preprint arXiv:2301.11798. (2023).