Semantic Segmentation of MRI Images for Brain Tumour Detection with ShuffleNet-Based UNet

Author:

Potnuru Mohankrishna,Naick B. Suribabu

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Networks and Communications,Computer Graphics and Computer-Aided Design,Computational Theory and Mathematics,Artificial Intelligence,General Computer Science

Reference40 articles.

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3. Bakas S, Reyes M, Jakab A, Bauer S, Rempfler M, Crimi A, Eaton-Rosen Z (2018) Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXiv preprint arXiv:1811.02629.

4. Wang G, Zhang S, Xie H, Metaxas DN, Gu L. A homotopy-based sparse representation for fast and accurate shape prior modeling in liver surgical planning. Med Image Anal. 2015;19(1):176–86.

5. Grosgeorge D, Petitjean C, Dacher JN, Ruan S. Graph cut segmentation with a statistical shape model in cardiac MRI. Comput Vis Image Underst. 2013;117(9):1027–35.

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