Detection of Vocal Cord Ulcer Using Advanced 3D ST Volumetric Segmentation Net Architecture

REFERENCES

• H. Badakhshannoory, “Saeedi P: A model-based validation scheme for organ segmentation in ct scan volumes,” IEEE Trans. Biomed. Eng., Vol. 58, pp. 2681–2693, 2011.
   PubMed Web of Science ®Google Scholar
• C. Li, W. Chen, and Y. Tan, “Point-sampling method based on 3D U-Net architecture to reduce the influence of false positive and solve boundary blur problem in 3D CT image segmentation,” Appl. Sci., Vol. 10, no. 19, pp. 6838, 2020.
   Google Scholar
• J. W. Dankbaar, and F. A. Pameijer, “Vocal cord paralysis: Anatomy, imaging and pathology,” Insights. Imaging., Vol. 5, no. 6, pp. 743–751, 2014.
   PubMedGoogle Scholar
• K. M. Khairuzzaman, and S. Chaudhury, “Multilevel thresholding using grey wolf optimizer for image segmentation,” Expert. Syst. Appl., Vol. 86, pp. 64–76, 2017.
   Web of Science ®Google Scholar
• K. Suzuki, “Pixel-based machine learning in medical imaging,” Int. J. Biomed. Imaging., Vol. 2012, p. 18, 2012.
   Google Scholar
• A. Srivastava, D. Jha, and S. Chanda. “MSRF-Net: A multi-scale residual fusion network for biomedical image segmentation”.
   Google Scholar
• S. M. U. Talha, T. Mairaj, W. B. Yousuf, and J. A. Zahed, “Region-based segmentation and Wiener pilot-based novel amoeba denoising scheme for CT imaging,” Scanning, Vol. 2020, p. 12, 2020.
   Web of Science ®Google Scholar
• F. P. An, and J. E. Liu, “Medical image segmentation Algorithm based on optimized convolutional Neural network-adaptive dropout depth calculation,” Complexity, Vol. 2020, pp. 13, 2020.
   Web of Science ®Google Scholar
• J. Amin, et al., “3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks,” Complex Intell. Syst. Vol, 8 no. 4, pp. 3041–3051, 2021. doi:10.1007/s40747-021-00328-7.
   Web of Science ®Google Scholar
• F. Bertone, E. Robiolio, and C. F. Gervasio, “Vocal cord Ulcer following Endotracheal intubation for mechanical ventilation in COVID-19 pneumonia: A Case report from northern Italy,” Am. J. Case. Rep., Vol. 21, pp. e928126, 2020. Published 2020 Nov 4. doi:10.12659/AJCR.928126.
   PubMed Web of Science ®Google Scholar
• C. K. Cho, J. J. Kim, T. Y. Sung, S. M. Jung, and P. S. Kang, “Endotracheal intubation-related vocal cord ulcer following general anesthesia,” Korean. J. Anesthesiol., S147–S148, 2013. doi:10.4097/kjae.2013.65.6S.S147.
   PubMedGoogle Scholar
• C.-H. Huang, H.-Y. Wu, and Y.-L. Lin. HarDNet-MSEG: A simple encoder-decoder polyp segmentation neural network that achieves over 0.9 mean dice and 86 FPS, arXiv:2101.07172.
   Google Scholar
• J. Dolz, K. Gopinath, J. Yuan, H. Lombaert, C. Desrosiers, and I. B. Ayed, “HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation,” IEEE Trans. Med. Imaging, Vol. 38, no. 5, pp. 1116–1126, 2018.
   PubMed Web of Science ®Google Scholar
• M. R. Zare, A. Mueen, M. Awedh, and W. C. Seng, “Automatic classification of medical x-ray images: Hybrid generative-discriminative approach,” IET Image Proc., Vol. 7, no. 5, pp. 523–532, 2013.
   Web of Science ®Google Scholar
• A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Commun. ACM, Vol. 60, no. 6, pp. 1097–1105, 2012.
   Google Scholar
• Y. Zhang, H. Liu, and Q. Hu. “TransFuse: Fusing transformers and CNNs for medical image segmentation”, arXiv:2102.08005.
   Google Scholar
• N. K. Tomar, D. Jha, M. A. Riegler, and H. D. Johansen, “A feedback attention network for improved biomedical image segmentation,” IEEE Trans. Neural Netw. Learn. Syst, doi:10.48550/arXiv.2103.17235.
   Web of Science ®Google Scholar
• R. Zheng, Y. Zheng, and C. Dong-Ye, “Improved 3d U-Net for COVID-19 chest CT image segmentation,” Sci. Program., Vol. 2021, pp. 9, 2021.
   Web of Science ®Google Scholar
• X. Chen, M. Niemeijer, L. Zhang, K. Lee, and M. D. Abramoff, “Sonka M: 3D segmentation of fluid- associated abnormalities in retinal oct probability constrained graph-search-graph-cut,” IEEE Trans. Med. Imaging, Vol. 31, pp. 1521–1531, 2012.
   PubMed Web of Science ®Google Scholar
• L. E. Carvalho, A. C. Sobieranski, and A. von Wangenheim, “3D segmentation algorithms for computerized tomographic imaging: A systematic literature review,” J. Digit Imaging, Vol. 31, pp. 799–850, 2018.
   PubMed Web of Science ®Google Scholar
• Z. Lai, and H. Deng, “Medical image classification based on deep features extracted by deep model and statistic feature fusion with multilayer perceptron,” Comput. Intell. Neurosci., Vol. 2018, pp. 13, 2018.
   Web of Science ®Google Scholar
• H. Seo, C. Huang, M. Bassenne, R. Xiao, and L. Xing, “Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images,” IEEE Trans. Med. Imaging, Vol. 39, pp. 1316–1325, 2019.
   PubMed Web of Science ®Google Scholar