AUCseg: An Automatically Unsupervised Clustering Toolbox for 3D-Segmentation of High-Grade Gliomas in Multi-Parametric MR Images.
AUCseg: An Automatically Unsupervised Clustering Toolbox for 3D-Segmentation of High-Grade Gliomas in Multi-Parametric MR Images.
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AUCseg：用于多参数 MR 图像中高级别胶质瘤 3D 分割的自动无监督聚类工具箱
DOI：
10.3389/fonc.2021.679952
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发表时间：
2021
影响因子：
4.7
通讯作者：
Zhang XY
中科院分区：
文献类型：
--
作者：
Zhao B;Ren Y;Yu Z;Yu J;Peng T;Zhang XY
利用磁共振成像(MRI)数据分割高级别胶质瘤(HGG)在神经外科实践中具有临床意义，但也是一项具有挑战性的任务。目前，大多数分割方法都是带标签训练集的监督学习。虽然这些方法在大多数情况下都很有效，但它们通常需要耗时的手动标记和预先训练的模型。在这项工作中，我们提出了一个基于聚类算法和形态处理的自动无监督分割工具箱AUCseg。使用我们的工具箱，首先通过在T2-FLAIR图像上聚类来提取整个肿瘤。然后，基于肿瘤整体分割得到的模板，在增强后的T1加权图像(T1-CE)上采用聚类方法分割增强后的肿瘤。最后在T2加权图像上对坏死区进行形态处理或聚类分割。与K-Means、Mini-Batch K-Means和模糊C Means(FCM)相比，高斯混合模型(GMM)的聚类效果最好。我们在BraTS2018数据集中对我们的工具箱进行了多方面的评估，并演示了可以使用默认的超参数自动分割整个肿瘤、肿瘤核心和增强的肿瘤，Dice Score分别为0.8209、0.7087和0.7254。我们的工具箱对每个案例的计算时间约为22秒，比其他最先进的非监督方法至少快3倍。此外，我们的工具箱还可以通过手动设置超参数来执行半自动分割，这可以提高分割性能。我们的工具箱AUCseg在Github上公开可用。(https://github.com/Haifengtao/AUCseg).)
The segmentation of high-grade gliomas (HGG) using magnetic resonance imaging (MRI) data is clinically meaningful in neurosurgical practice, but a challenging task. Currently, most segmentation methods are supervised learning with labeled training sets. Although these methods work well in most cases, they typically require time-consuming manual labeling and pre-trained models. In this work, we propose an automatically unsupervised segmentation toolbox based on the clustering algorithm and morphological processing, named AUCseg. With our toolbox, the whole tumor was first extracted by clustering on T2-FLAIR images. Then, based on the mask acquired with whole tumor segmentation, the enhancing tumor was segmented on the post-contrast T1-weighted images (T1-CE) using clustering methods. Finally, the necrotic regions were segmented by morphological processing or clustering on T2-weighted images. Compared with K-means, Mini-batch K-means, and Fuzzy C Means (FCM), the Gaussian Mixture Model (GMM) clustering performs the best in our toolbox. We did a multi-sided evaluation of our toolbox in the BraTS2018 dataset and demonstrated that the whole tumor, tumor core, and enhancing tumor can be automatically segmented using default hyper-parameters with Dice score 0.8209, 0.7087, and 0.7254, respectively. The computing time of our toolbox for each case is around 22 seconds, which is at least 3 times faster than other state-of-the-art unsupervised methods. In addition, our toolbox has an option to perform semi-automatic segmentation via manually setup hyper-parameters, which could improve the segmentation performance. Our toolbox, AUCseg, is publicly available on Github. (https://github.com/Haifengtao/AUCseg).
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