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Review of Automatic Segmentation of MRI Based Brain Tumour using U-Net Architecture

机译:基于U-Net架构的基于MRI的脑肿瘤自动分割的综述

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Segmentation of brain tumour from the surrounding healthy brain tissues by a radiologist is a tedious task. For a safe brain surgery, it is essential to define the contour of brain tumour, for complete resection of tumour. Active research is being carried out in automatic tumour segmentation using deep learning networks for precise segmentation of tumour components. Deep learning networks are more effective at learning patterns and the performance of deep network increases when trained with more data. This paper reviews the automated segmentation of brain tumour in MRI images by using U-Net, and the deep learning network architecture. U-Net is a convolutional neural network architecture developed for segmentation of biomedical images.
机译:放射科医生从周围健康的脑组织中分割脑肿瘤是一项繁琐的任务。为了进行安全的脑部手术,必须定义脑部肿瘤的轮廓,以完全切除肿瘤。正在使用深度学习网络对肿瘤成分进行精确分割的自动肿瘤分割中进行积极的研究。深度学习网络在学习模式上更有效,并且在接受更多数据训练后,深度网络的性能会提高。本文回顾了使用U-Net在MRI图像中进行脑肿瘤的自动分割以及深度学习网络体系结构。 U-Net是为生物医学图像分割而开发的卷积神经网络体系结构。

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