Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI.
Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI.
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DOI：
10.3389/fninf.2020.610967
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发表时间：
2020
影响因子：
3.5
通讯作者：
Zhao S
中科院分区：
文献类型：
--
作者：
Zeng C;Gu L;Liu Z;Zhao S
近年来，已有多篇文献综述了多发性硬化症（MS）病变自动分割的方法。然而，目前还没有文献对基于深度学习的MS病灶分割方法进行系统和单独的综述。虽然之前的综述也包括了基于深度学习的方法，但是有一些基于深度学习的方法他们并没有综述。此外，他们对深度学习方法的回顾并没有深入到卷积神经网络（CNN）的具体类别。他们只是笼统地回顾了这些方法，如监督策略、输入数据处理策略等。本文系统回顾了基于深度学习的多发性硬化症病灶自动分割的相关文献。回顾了基于深度学习的算法，通过其CNN风格将其分为两类，通过我们的调查和分析，也将给出它们的优缺点。我们通过两个指标：骰子相似系数（DSC）和阳性预测值（PPV）对所回顾的方法进行了定量比较。最后，讨论了深度学习在MS病灶分割中的未来应用方向。
In recent years, there have been multiple works of literature reviewing methods for automatically segmenting multiple sclerosis (MS) lesions. However, there is no literature systematically and individually review deep learning-based MS lesion segmentation methods. Although the previous review also included methods based on deep learning, there are some methods based on deep learning that they did not review. In addition, their review of deep learning methods did not go deep into the specific categories of Convolutional Neural Network (CNN). They only reviewed these methods in a generalized form, such as supervision strategy, input data handling strategy, etc. This paper presents a systematic review of the literature in automated multiple sclerosis lesion segmentation based on deep learning. Algorithms based on deep learning reviewed are classified into two categories through their CNN style, and their strengths and weaknesses will also be given through our investigation and analysis. We give a quantitative comparison of the methods reviewed through two metrics: Dice Similarity Coefficient (DSC) and Positive Predictive Value (PPV). Finally, the future direction of the application of deep learning in MS lesion segmentation will be discussed.
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影响因子：
5.7
作者：
Carass A;Roy S;Jog A;Cuzzocreo JL;Magrath E;Gherman A;Button J;Nguyen J;Prados F;Sudre CH;Jorge Cardoso M;Cawley N;Ciccarelli O;Wheeler-Kingshott CAM;Ourselin S;Catanese L;Deshpande H;Maurel P;Commowick O;Barillot C;Tomas-Fernandez X;Warfield SK;Vaidya S;Chunduru A;Muthuganapathy R;Krishnamurthi G;Jesson A;Arbel T;Maier O;Handels H;Iheme LO;Unay D;Jain S;Sima DM;Smeets D;Ghafoorian M;Platel B;Birenbaum A;Greenspan H;Bazin PL;Calabresi PA;Crainiceanu CM;Ellingsen LM;Reich DS;Prince JL;Pham DL
通讯作者：
Pham DL
影响因子：
10.6
作者：
Brosch, Tom;Tang, Lisa Y. W.;Tam, Roger
通讯作者：
Tam, Roger
影响因子：
4.3
作者：
Alshayeji, Mohammad H.;Al-Rousan, Mohammad A.;Abed, Sa'ed
通讯作者：
Abed, Sa'ed
DOI：
10.1007/978-3-030-13969-8_1
发表时间：
2019-01-01
期刊：
DEEP LEARNING AND CONVOLUTIONAL NEURAL NETWORKS FOR MEDICAL IMAGING AND CLINICAL INFORMATICS
影响因子：
--
作者：
Cai, Jinzheng;Lu, Le;Yang, Lin
通讯作者：
Yang, Lin
影响因子：
5.8
作者：
Gabr, Refaat E.;Coronado, Ivan;Narayana, Ponnada A.
通讯作者：
Narayana, Ponnada A.