Tsai Jang-Zern, Peng Syu-Jyun, Chen Yu-Wei, Wang Kuo-Wei, Wu Hsiao-Kuang, Lin Yun-Yu, Lee Ying-Ying, Chen Chi-Jen, Lin Huey-Juan, Smith Eric Edward, Yeh Poh-Shiow, Hsin Yue-Loong
Department of Electrical Engineering, National Central University, Jhongli City, Taoyuan County 32001, Taiwan.
Department of Computer Science and Information Engineering, National Central University, Jhongli City, Taoyuan County 32001, Taiwan ; Department of Neurology, Landseed Hospital, Pingzhen City, Taoyuan County 32449, Taiwan ; Department of Neurology, National Taiwan University Hospital, Taipei City 10002, Taiwan.
Biomed Res Int. 2014;2014:963032. doi: 10.1155/2014/963032. Epub 2014 Mar 12.
Determination of the volumes of acute cerebral infarct in the magnetic resonance imaging harbors prognostic values. However, semiautomatic method of segmentation is time-consuming and with high interrater variability. Using diffusion weighted imaging and apparent diffusion coefficient map from patients with acute infarction in 10 days, we aimed to develop a fully automatic algorithm to measure infarct volume. It includes an unsupervised classification with fuzzy C-means clustering determination of the histographic distribution, defining self-adjusted intensity thresholds. The proposed method attained high agreement with the semiautomatic method, with similarity index 89.9 ± 6.5%, in detecting cerebral infarct lesions from 22 acute stroke patients. We demonstrated the accuracy of the proposed computer-assisted prompt segmentation method, which appeared promising to replace the laborious, time-consuming, and operator-dependent semiautomatic segmentation.
在磁共振成像中测定急性脑梗死体积具有预后价值。然而，半自动分割方法耗时且评分者间变异性高。利用10天内急性梗死患者的扩散加权成像和表观扩散系数图，我们旨在开发一种全自动算法来测量梗死体积。该算法包括基于模糊C均值聚类确定直方图分布的无监督分类，定义自我调整的强度阈值。在检测22例急性中风患者的脑梗死病变时，该方法与半自动方法高度一致，相似指数为89.9±6.5%。我们证明了所提出的计算机辅助快速分割方法的准确性，该方法有望取代费力、耗时且依赖操作者的半自动分割方法。
