Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Nyúl LG, Udupa JK, Zhang X (2000) New variants of a method of MRI scale standardization. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4ITK: improved N3 bias correction. Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Lanczi L (2014) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Radiat Plasma Med Sci 3(2):162–169 Guo Z, Li X, Huang H, Guo N, Li Q (2019) Deep learning-based image segmentation on multimodal medical imaging. Springer, Singapore, pp 313–321Īcharya UR, Fernandes SL, WeiKoh JE, Ciaccio EJ, Fabell MKM, Tanik UJ, Yeong CH (2019) Automated detection of Alzheimer’s disease using brain MRI images–a study with various feature extraction techniques. In: Proceedings of 2nd international conference on micro-electronics, electromagnetics and telecommunications. Rajinikanth V, Fernandes SL, Bhushan B, Sunder NR (2018) Segmentation and analysis of brain tumor using Tsallis entropy and regularised level set. Rajinikanth V, Satapathy SC, Fernandes SL, Nachiappan S (2017) Entropy based segmentation of tumor from brain MR images–a study with teaching learning based optimization. Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI images. Tustison NJ, Shrinidhi KL, Wintermark M, Durst CR, Kandel BM, Gee JC, Avants BB (2015) Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. The new dataset provides 0.87, 0.91, and 0.79 for the three regions, respectively. According to the second dataset, the three regions had an average of 0.88, 0.77, and 0.65, respectively. It achieves good performances compared to new methods, with Dice scores of 0.86 for the Whole Tumor, 0.82 for Tumor Core, and 0.6 for Enhancing Tumor based on the first dataset. The presented scheme is tested using recent datasets (BraTS 2017, 2018, and 2020). A new segmentation scheme is suggested using Convolutional Neural Networks (CNN).

For these reasons, we are motivated to suggest a new segmentation approach using deep learning. Automatic and accurate segmentation technique becomes a challenging problem since these tumors can take a variety of sizes, contrast, and shape. This treatment is manually performed which needs time and represents a tedious task. The treatment planning is decided after the analysis of MRI data to assess tumors. In their highest grade, it can lead to a very short life. Gliomas represent the most common and aggressive brain tumors. The quantitative analysis of brain magnetic resonance imaging (MRI) represents a tiring routine and enormously on accurate segmentation of some brain regions.

Faqt score 79