Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.
Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification.
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DOI：
10.1371/journal.pone.0125143
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
García-Gómez JM
中科院分区：
文献类型：
--
作者：
Juan-Albarracín J;Fuster-Garcia E;Manjón JV;Robles M;Aparici F;Martí-Bonmatí L;García-Gómez JM
自动脑肿瘤分割已成为未来脑肿瘤治疗的关键组成部分。目前，大多数脑肿瘤分割方法都是从监督学习的角度出发的，这需要一个标记的训练数据集来推断类的模型。这些模型的性能直接取决于训练语料库的大小和质量，其检索成为一个繁琐和耗时的任务。另一方面，无监督方法避免了这些限制，但通常无法达到与有监督方法可比的结果。在这个意义上，我们提出了一个自动的无监督的方法，脑肿瘤分割的基础上解剖磁共振（MR）图像。四个无监督分类算法，按其结构化或非结构化条件分组，在我们的管道中进行了评估。考虑到非结构化算法，我们评估了K-均值，模糊K-均值和高斯混合模型（GMM），而作为结构化分类算法，我们评估了高斯隐马尔可夫随机场（GHMRF）。提出了一种基于组织概率图支持的统计方法的自动后处理方法，用于在分割后自动识别肿瘤类别。我们使用公共BRAin肿瘤分割（BRATS）2013测试和排行榜数据集评估了我们的脑肿瘤分割方法。我们的方法基于GMM模型，提高了大多数的监督方法与排行榜集评估的结果，并达到排名第二的位置。我们的变体的GHMRF的基础上实现的第一个位置在测试排名的无监督的方法和第七的位置在一般的测试排名，这证实了该方法作为一个可行的替代脑肿瘤分割。
Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.
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影响因子：
15.9
作者：
Dolecek, Therese A.;Propp, Jennifer M.;Kruchko, Carol
通讯作者：
Kruchko, Carol
影响因子：
5.7
作者：
Fonov V;Evans AC;Botteron K;Almli CR;McKinstry RC;Collins DL;Brain Development Cooperative Group
通讯作者：
Brain Development Cooperative Group
DOI：
10.1111/j.2517-6161.1977.tb01600.x
发表时间：
1977-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
DEMPSTER, AP;LAIRD, NM;RUBIN, DB
通讯作者：
RUBIN, DB
影响因子：
10.9
作者：
Avants, B. B.;Epstein, C. L.;Gee, J. C.
通讯作者：
Gee, J. C.
影响因子：
7.5
作者：
Fletcher-Heath, LM;Hall, LO;Murtagh, FR
通讯作者：
Murtagh, FR