MIRALab - University of Geneva, Battelle, Building A, 7 Route de Drize, CH-1227 Carouge, Switzerland.
Med Image Anal. 2010 Jun;14(3):291-302. doi: 10.1016/j.media.2010.01.006. Epub 2010 Mar 1.
The automatic segmentation of the musculoskeletal system from medical images is a particularly challenging task, due to its morphological complexity, its large variability in the population and its potentially large deformations. In this paper we propose a novel approach for musculoskeletal segmentation and registration based on simplex meshes. Such discrete models have already proven to be efficient and versatile for medical image segmentation. We extend the current framework by introducing a multi-resolution approach and a reversible medial representation, in order to reduce the complexity of geometric and non-penetration constraints computation. Our framework allows both inter and intra-patient registration (involving both rigid and elastic matching). We also show that the introduced representations facilitate morphological analysis. As a case study, we demonstrate that muscles, bones, ligaments and cartilages of the hip and the thigh can be registered at an interactive frame rate, in a time-efficient way (<30min), with a satisfactory accuracy ( approximately 1.5mm), and with a minimal amount of manual tasks.
从医学图像中自动分割肌肉骨骼系统是一项极具挑战性的任务,这主要是由于其形态结构复杂、人群中存在较大的变异性以及其可能发生较大的变形。在本文中,我们提出了一种新的基于单纯形网格的肌肉骨骼分割和配准方法。这种离散模型已经被证明在医学图像分割中是高效和通用的。我们通过引入多分辨率方法和可逆中轴表示来扩展当前的框架,以降低几何和非穿透约束计算的复杂性。我们的框架允许进行内外患者配准(涉及刚性和弹性匹配)。我们还表明,所引入的表示形式有助于形态分析。作为一个案例研究,我们证明了可以以交互帧率、高效的方式(<30min)、具有令人满意的准确性(约 1.5mm)和最小的手动任务量,对髋关节和大腿的肌肉、骨骼、韧带和软骨进行注册。