Better Diffusion Segmentation in Acute Ischemic Stroke Through Automatic Tree Learning Anomaly Segmentation.
Better Diffusion Segmentation in Acute Ischemic Stroke Through Automatic Tree Learning Anomaly Segmentation.
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DOI：
10.3389/fninf.2018.00021
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发表时间：
2018
影响因子：
3.5
通讯作者：
Mouridsen K
中科院分区：
文献类型：
--
作者：
Boldsen JK;Engedal TS;Pedraza S;Cho TH;Thomalla G;Nighoghossian N;Baron JC;Fiehler J;Østergaard L;Mouridsen K
中风是全球第二大常见死因，2015年造成624万人死亡（约占所有死亡人数的11%）。四分之三的中风幸存者患有长期残疾，因为许多人无法返回之前的工作或独立生活。87%的中风是缺血性的。随着缺血性脑组织体积的增加在发病后数小时内发展为永久性梗死，立即治疗对于增加患者良好临床结局的可能性至关重要。对中风患者进行积极治疗的分类需要分别评估可挽救和不可逆损伤组织的体积。在磁共振成像（MRI）中，弥散加权成像通常用于评估永久性损伤组织（核心病变）的程度。为了加速和标准化急性卒中管理中的决策制定，我们提出了一种全自动算法，ATLAS，用于描绘核心病变。我们比较性能广泛使用的阈值为基础的方法，以及最近提出的国家的最先进的算法：COMBAT中风。ATLAS是一种经过训练的机器学习算法，用于匹配人类专家的病变描绘。该算法利用决策树沿着与空间预和后正则化，以勾勒病变。作为输入数据，该算法从I-Know多中心研究的108例急性前循环卒中患者中获取图像。我们使用留一交叉验证将数据分为训练数据和测试数据，以评估独立患者的性能。性能通过Dice指数进行量化。ATLAS算法的中值Dice系数为0.6122，显著高于COMBAT Stroke，中值Dice系数为0.5636（p < 0.0001），并且最佳可能执行方法基于扩散加权图像的阈值（中值Dice系数：0.3951）或表观扩散系数（中值Dice系数：0.2839）。此外，将ATLAS分割的体积与专家分割的体积进行了比较，得出残差的标准差为10.25 ml，而COMBAT Stroke为17.53 ml。由于对永久性损伤组织体积的准确定量在急性卒中患者中至关重要，因此ATLAS可能有助于更优化患者分类，以进行积极或支持性治疗。
Stroke is the second most common cause of death worldwide, responsible for 6.24 million deaths in 2015 (about 11% of all deaths). Three out of four stroke survivors suffer long term disability, as many cannot return to their prior employment or live independently. Eighty-seven percent of strokes are ischemic. As an increasing volume of ischemic brain tissue proceeds to permanent infarction in the hours following the onset, immediate treatment is pivotal to increase the likelihood of good clinical outcome for the patient. Triaging stroke patients for active therapy requires assessment of the volume of salvageable and irreversible damaged tissue, respectively. With Magnetic Resonance Imaging (MRI), diffusion-weighted imaging is commonly used to assess the extent of permanently damaged tissue, the core lesion. To speed up and standardize decision-making in acute stroke management we present a fully automated algorithm, ATLAS, for delineating the core lesion. We compare performance to widely used threshold based methodology, as well as a recently proposed state-of-the-art algorithm: COMBAT Stroke. ATLAS is a machine learning algorithm trained to match the lesion delineation by human experts. The algorithm utilizes decision trees along with spatial pre- and post-regularization to outline the lesion. As input data the algorithm takes images from 108 patients with acute anterior circulation stroke from the I-Know multicenter study. We divided the data into training and test data using leave-one-out cross validation to assess performance in independent patients. Performance was quantified by the Dice index. The median Dice coefficient of ATLAS algorithm was 0.6122, which was significantly higher than COMBAT Stroke, with a median Dice coefficient of 0.5636 (p < 0.0001) and the best possible performing methods based on thresholding of the diffusion weighted images (median Dice coefficient: 0.3951) or the apparent diffusion coefficient (median Dice coefficeint: 0.2839). Furthermore, the volume of the ATLAS segmentation was compared to the volume of the expert segmentation, yielding a standard deviation of the residuals of 10.25 ml compared to 17.53 ml for COMBAT Stroke. Since accurate quantification of the volume of permanently damaged tissue is essential in acute stroke patients, ATLAS may contribute to more optimal patient triaging for active or supportive therapy.
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