Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.
Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.
复制标题
使用多参数MRI的无监督分类方法进行脑肿瘤分割的比较。
DOI：
10.1016/j.nicl.2016.09.021
复制
发表时间：
2016
影响因子：
4.2
通讯作者：
Van Huffel, S.
中科院分区：
文献类型：
--
作者：
Sauwen, N.;Acou, M.;Van Cauter, S.;Sima, D. M.;Veraart, J.;Maes, F.;Himmelreich, U.;Achten, E.;Van Huffel, S.
关键词：
对于高级别胶质瘤（HGGs）来说，肿瘤分割是一项特别具有挑战性的任务，因为它们是肿瘤学中最异质性的肿瘤之一。准确描述病变及其主要亚成分有助于优化治疗计划、预后和随访。常规MRI （cMRI）是人工分割的首选成像方式，在绝大多数自动分割研究中也被考虑。先进的MRI模式，如灌注加权成像（PWI）、弥散加权成像（DWI）和磁共振波谱成像（MRSI）已经显示出它们在肿瘤组织表征中的附加价值，因此最近有人建议将不同的MRI模式组合成一种多参数MRI （MP-MRI）方法，用于脑肿瘤分割。在本文中，我们比较了几种基于MP-MRI数据的无监督分类方法，包括cMRI， DWI， MRSI和PWI，用于HGG分割的性能。来自不同医院的两个独立的MP-MRI数据集具有不同的获取方案。我们证明了先前为MP-MRI肿瘤分割引入的分层非负矩阵分解变体在两个数据集上的病理组织类别的平均dice分数方面具有最佳性能。将无监督分类算法应用于多参数MRI数据集的脑肿瘤分割。报告的平均骰子分数在最先进的分割算法的范围内。分层NMF在大多数组织类别的平均dice分数方面获得了最好的分割结果。
Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets. Unsupervised classification algorithms are applied for brain tumor segmentation on multi-parametric MRI datasets. Reported mean Dice-scores are in the range of state-of-the-art segmentation algorithms. Hierarchical NMF obtained the best segmentation results in terms of mean Dice-scores for most of the tissue classes.
登录
查看更多内容
影响因子：
15.9
作者：
Jones TL;Byrnes TJ;Yang G;Howe FA;Bell BA;Barrick TR
通讯作者：
Barrick TR
影响因子：
2.1
作者：
Gillis, Nicolas
通讯作者：
Gillis, Nicolas
影响因子：
15.9
作者：
Hu LS;Eschbacher JM;Heiserman JE;Dueck AC;Shapiro WR;Liu S;Karis JP;Smith KA;Coons SW;Nakaji P;Spetzler RF;Feuerstein BG;Debbins J;Baxter LC
通讯作者：
Baxter LC
影响因子：
--
作者：
Ion-Margineanu A;Van Cauter S;Sima DM;Maes F;Van Gool SW;Sunaert S;Himmelreich U;Van Huffel S
通讯作者：
Van Huffel S
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