Brain Tumor Segmentation u-net 3+

The measurement of tumour extent is a significant difficulty in brain tumour treatment planning and quantitative evaluation. Non-invasive magnetic resonance imaging (MRI) has evolved as a first-line diagnostic method for brain malignancies that does not require ionising radiation. The manual segmentation of brain tumour extent from 3D MRI volumes is a time-consuming job that heavily relies on the operator's knowledge. In this context, a dependable fully automatic segmentation approach for brain tumour segmentation is required for accurate tumour extent determination. We offer a fully automatic method for brain tumour segmentation in this article, which is based on U-Net-based deep convolutional networks. Our technique was tested using the Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which included 220 cases of high-grade brain tumour and 54 cases of low-grade tumour. Cross-validation has demonstrated that our method may efficiently obtain promising segmentation.

Keywords: - Brain U-Net Segmentation, Brain image dataset, Image segmentation, and segmentation algorithms.