A Survey of Brain Tumor Segmentation and Classification Algorithms.
A Survey of Brain Tumor Segmentation and Classification Algorithms.
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DOI：
10.3390/jimaging7090179
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发表时间：
2021-09-06
影响因子：
3.2
通讯作者：
Debelee TG
中科院分区：
文献类型：
--
作者：
Biratu ES;Schwenker F;Ayano YM;Debelee TG
单个个体的脑部磁共振成像 (MRI) 扫描由 3D 解剖视图中的多个切片组成。因此，从磁共振（MR）图像中手动分割脑肿瘤是一项具有挑战性且耗时的任务。此外，MRI 扫描的自动脑肿瘤分类是非侵入性的，因此可以避免活检并使诊断过程更安全。自本世纪初和九十年代末以来，研究界为提出自动脑肿瘤分割和分类方法付出了巨大的努力。因此，该领域有大量文献关注使用区域增长、传统机器学习和深度学习方法进行分割。同样，在将脑肿瘤分类为各自的组织学类型方面也进行了许多任务，并取得了令人印象深刻的结果。考虑到最先进的方法及其性能，本文的目的是对最近提出的三种主要脑肿瘤分割和分类模型技术（即区域生长、浅层机器学习和深度学习）进行全面调查。本次调查中包含的既定工作还涵盖了技术方面，例如不同方法的优缺点、预处理和后处理技术、特征提取、数据集和模型的性能评估指标。
A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models’ performance evaluation metrics.
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影响因子：
3.9
作者：
Ali, Mahnoor;Gilani, Syed Omer;Jamil, Mohsin
通讯作者：
Jamil, Mohsin
DOI：
10.4028/www.scientific.net/jera.51.131
发表时间：
2020-01-01
影响因子：
0.7
作者：
Afework, Yidnekachew Kibru;Debelee, Taye Girma
通讯作者：
Debelee, Taye Girma
磁场强度对脑成像，体外和体内研究中磁共振成像放射素学特征的影响。
影响因子：
4.7
作者：
Ammari S;Pitre-Champagnat S;Dercle L;Chouzenoux E;Moalla S;Reuze S;Talbot H;Mokoyoko T;Hadchiti J;Diffetocq S;Volk A;El Haik M;Lakiss S;Balleyguier C;Lassau N;Bidault F
通讯作者：
Bidault F
影响因子：
3.2
作者：
Biratu ES;Schwenker F;Debelee TG;Kebede SR;Negera WG;Molla HT
通讯作者：
Molla HT
影响因子：
3.9
作者：
Afshar, Parnian;Mohammadi, Arash;Plataniotis, Konstantinos N.
通讯作者：
Plataniotis, Konstantinos N.