Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, Valencia 46022, Spain.
Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, Valencia 46022, Spain.
Neuroimage Clin. 2020;25:102184. doi: 10.1016/j.nicl.2020.102184. Epub 2020 Jan 15.
Parkinson is a very prevalent neurodegenerative disease impacting the life of millions of people worldwide. Although its cause remains unknown, its functional and structural analysis is fundamental to advance in the search of a cure or symptomatic treatment. The automatic segmentation of deep brain structures related to Parkinson`s disease could be beneficial for the follow up and treatment planning. Unfortunately, there is not broadly available segmentation software to automatically measure Parkinson related structures. In this paper, we present a novel pipeline to segment three deep brain structures related to Parkinson's disease (substantia nigra, subthalamic nucleus and red nucleus). The proposed method is based on the multi-atlas label fusion technology that works on standard and high-resolution T2-weighted images. The proposed method also includes as post-processing a new neural network-based error correction step to minimize systematic segmentation errors. The proposed method has been compared to other state-of-the-art methods showing competitive results in terms of accuracy and execution time.
帕金森病是一种非常普遍的神经退行性疾病，影响着全球数百万人的生活。尽管其病因尚不清楚，但对其功能和结构的分析对于寻找治疗方法或对症治疗至关重要。与帕金森病相关的深部脑结构的自动分割可能有助于后续治疗计划。不幸的是，目前还没有广泛可用的自动分割软件来自动测量帕金森相关结构。在本文中，我们提出了一种新的流水线，用于分割与帕金森病相关的三个深部脑结构（黑质、丘脑底核和红核）。所提出的方法基于多图谱标签融合技术，适用于标准和高分辨率 T2 加权图像。该方法还包括一个基于神经网络的新的误差校正步骤作为后处理，以最小化系统分割误差。所提出的方法与其他最先进的方法进行了比较，在准确性和执行时间方面都具有竞争力的结果。
