基于图谱的病理性脑部磁共振图像分割,采用病变生长模型

Atlas-based segmentation of pathological MR brain images using a model of lesion growth.

作者信息

Cuadra Meritxell Bach, Pollo Claudio, Bardera Anton, Cuisenaire Olivier, Villemure Jean-Guy, Thiran Jean-Philippe

机构信息

Signal Processing Institute, Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland.

出版信息

IEEE Trans Med Imaging. 2004 Oct;23(10):1301-14. doi: 10.1109/TMI.2004.834618.

Abstract

We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.

摘要

我们提出了一种在存在大型占位性肿瘤的情况下进行脑图谱变形的方法,该方法基于病变生长的先验模型,该模型假设病变从其起始点呈径向扩展。我们的方法包括三个步骤。首先,仿射配准使图谱与患者达到全局对应。然后,将合成肿瘤植入脑图谱中,为病变提供一个模板。最后一步是对植入图谱进行变形,结合一种源自光流原理的方法和病变生长模型。结果表明,该方法实现了良好的配准,并且可以应用于严重变形脑内结构和子结构的自动分割,在神经外科、放射外科和放射治疗中具有重要的医学应用价值。

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