基于机器学习的脑MR图像病灶分割方法综述

Evgin Göçeri, E. Durá, M. Günay
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引用次数: 6

摘要

脑部病变是危及生命的疾病。传统的脑部病变诊断是由神经放射科医生通过视觉进行的。如今,磁共振成像的先进技术和进步提供了计算机辅助诊断,使用自动化方法可以从不同的医学图像中检测和分割异常区域。在几种技术中,基于机器学习的方法灵活高效。因此,在本文中,我们介绍了应用监督和无监督机器学习技术从磁共振图像中检测和分割脑病变的技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Review on Machine Learning Based Lesion Segmentation Methods from Brain MR Images
Brain lesions are life threatening diseases. Traditional diagnosis of brain lesions is performed visually by neuro-radiologists. Nowadays, advanced technologies and the progress in magnetic resonance imaging provide computer aided diagnosis using automated methods that can detect and segment abnormal regions from different medical images. Among several techniques, machine learning based methods are flexible and efficient. Therefore, in this paper, we present a review on techniques applied for detection and segmentation of brain lesions from magnetic resonance images with supervised and unsupervised machine learning techniques.
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