Manjón José V, Eskildsen Simon F, Coupé Pierrick, Romero José E, Collins D Louis, Robles Montserrat
Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.
Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Nørrebrogade 44, 8000 Aarhus, Denmark.
Int J Biomed Imaging. 2014;2014:820205. doi: 10.1155/2014/820205. Epub 2014 Sep 28.
Automatic and accurate methods to estimate normalized regional brain volumes from MRI data are valuable tools which may help to obtain an objective diagnosis and followup of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume (ICV) is often used for normalization. However, the high variability of brain shape and size due to normal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV estimation problem challenging. In this paper, we present a new approach to perform ICV extraction based on the use of a library of prelabeled brain images to capture the large variability of brain shapes. To this end, an improved nonlocal label fusion scheme based on BEaST technique is proposed to increase the accuracy of the ICV estimation. The proposed method is compared with recent state-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden.
从MRI数据中自动准确估计标准化区域脑容量的方法是有价值的工具，有助于对许多神经系统疾病进行客观诊断和随访。为了估计此类区域脑容量，颅内腔容积（ICV）常被用于标准化。然而，由于个体间正常变异、一生中发生的正常变化以及疾病导致的异常变化，脑形状和大小的高度变异性使得ICV估计问题具有挑战性。在本文中，我们提出了一种新方法，基于使用预标记脑图像库来捕捉脑形状的巨大变异性来进行ICV提取。为此，提出了一种基于BEaST技术的改进非局部标签融合方案，以提高ICV估计的准确性。将所提出的方法与最近的先进方法进行了比较，结果表明，该方法在准确性和可重复性方面均有改进，同时保持了较低的计算负担。
