Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images.
Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images.
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DOI：
10.1016/j.nicl.2015.05.003
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发表时间：
2015
影响因子：
4.2
通讯作者：
Smeets, Dirk
中科院分区：
文献类型：
--
作者：
Jain, Saurabh;Sima, Diana M.;Ribbens, Annemie;Cambron, Melissa;Maertens, Anke;Van Hecke, Wim;De Mey, Johan;Barkhof, Frederik;Steenwijk, Martijn D.;Daams, Marita;Maes, Frederik;Van Huffel, Sabine;Vrenken, Hugo;Smeets, Dirk
磁共振成像（MRI）上白色病变的部位和范围是多发性硬化（MS）诊断、随访和预后的重要指标。临床试验表明，定量值，如病变体积，是有意义的MS预后。然而，用于病变分割的手动病变描绘是耗时的，并且受到观察者可变性的影响。在本文中，我们提出了MSTOM，一个准确和可靠的自动方法病变分割的基础上MRI，独立的扫描仪或采集协议，不需要任何训练数据。在MSET中，在概率模型中使用3D T1加权和FLAIR MR图像来检测作为正常脑的异常值的白色（WM）病变，同时将脑组织分割成灰质、WM和脑脊液。实际的病变分割是基于关于病变位置（WM内）和外观（FLAIR上的高信号）的先验知识进行的。通过将其输出与MS患者的20个MRI数据集的专家参考分割进行比较来评估MSET的准确性。MSVR和专家病变分割之间的空间重叠（Dice）为0.67 ± 0.11。组内相关系数（ICC）等于0.8，表明MS和专家标签之间的体积一致性良好。基于10名MS患者，在三种不同的扫描仪上以短间隔扫描两次，评价MS病灶体积的再现性。通过空间重叠和绝对病变体积差来评价每台扫描仪上第一次和第二次扫描之间的一致性。空间重叠为0.69 ± 0.14，两次扫描之间的绝对总病变体积差为0.54 ± 0.58 ml。最后，与使用默认参数设置应用于相同数据的其他公开可用的MS病变分割算法相比，MSTK的准确性和再现性更佳。MSPG是一种新的基于MRI的白色病变自动分割方法。MSTOM使用3D T1加权和FLAIR MR图像执行无监督分割。在两个MS数据集上验证了MS方法，并显示出良好的准确性和重现性。与其他公开可用的MS病变分割算法相比，MSVR更胜一筹。
The location and extent of white matter lesions on magnetic resonance imaging (MRI) are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS). Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM) lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM) and the appearance (hyperintense on FLAIR) of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice) between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC) equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default parameter settings. MSmetrix is a new automatic method for white matter lesion segmentation based on MRI. MSmetrix performs unsupervised segmentation using 3D T1-weighted and FLAIR MR images. MSmetrix is validated on two MS datasets and shows good accuracy and reproducibility. MSmetrix compares favourably with other publicly available MS lesion segmentation algorithms.
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影响因子：
4.2
作者：
Steenwijk, Martijn D.;Pouwels, Petra J. W.;Daams, Marita;van Dalen, Jan Willem;Caan, Matthan W. A.;Richard, Edo;Barkhof, Frederik;Vrenken, Hugo
通讯作者：
Vrenken, Hugo
影响因子：
11.2
作者：
Polman CH;Reingold SC;Banwell B;Clanet M;Cohen JA;Filippi M;Fujihara K;Havrdova E;Hutchinson M;Kappos L;Lublin FD;Montalban X;O'Connor P;Sandberg-Wollheim M;Thompson AJ;Waubant E;Weinshenker B;Wolinsky JS
通讯作者：
Wolinsky JS
影响因子：
10.6
作者：
Van Leemput, K;Maes, F;Suetens, P
通讯作者：
Suetens, P
影响因子：
4.8
作者：
DICE, LR
通讯作者：
DICE, LR
影响因子：
4.4
作者：
Chard, Declan T.;Jackson, Jonathan S.;Wheeler-Kingshott, Claudia A. M.
通讯作者：
Wheeler-Kingshott, Claudia A. M.