AS-3DFCN: Automatically Seeking 3DFCN-Based Brain Tumor Segmentation-Reference-Cited by-同舟云学术

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AS-3DFCN: Automatically Seeking 3DFCN-Based Brain Tumor Segmentation

Published:2023-07-18 Issue: Volume: Page:
ISSN:1866-9956
Container-title:Cognitive Computation
language:en
Short-container-title:Cogn Comput

Author:

Liu Ruihua^ORCID,Nan Haoyu,Zou Yangyang,Xie Ting

Funder

Natural Science Foundation Project of Chongqing, Chongqing Science and Technology Commission

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience,Computer Science Applications,Computer Vision and Pattern Recognition

Link

https://link.springer.com/content/pdf/10.1007/s12559-023-10168-x.pdf

Reference38 articles.

1. Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathological. 2007;114(2):97–109.

2. Kaur T, Saini BS, Gupta S. A novel fully automatic multilevel thresholding technique based on optimized intuitionistic fuzzy sets and tsallis entropy for MR brain tumor image segmentation. Australas Phys Eng Sci Med. 2018;41(1):41–58.

3. Lei X, Yu X, Chi J, Wang Y, Zhang J, Wu C. Brain tumor segmentation in MR images using a sparse constrained level set algorithm. Expert Syst Appl. 2021;168:114262.

4. Sheela C, Suganthi G. Morphological edge detection and brain tumor segmentation in magnetic resonance (MR) images based on region growing and performance evaluation of modified Fuzzy C-Means (FCM) algorithm. Multimed Tools Appl. 2020;79(25):17483–96.

5. Siciarz P, McCurdy B. U-net architecture with embedded Inception-ResNet-v2 image encoding modules for automatic segmentation of organs-at-risk in head and neck cancer radiation therapy based on computed tomography scans. Phys Med Biol. 2022;67(11):115007.

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