Gilles Benjamin, Moccozet Laurent, Magnenat-Thalmann Nadia
MIRALab, University of Geneva, CH-1211 Geneva, Switzerland.
Med Image Comput Comput Assist Interv. 2006;9(Pt 1):289-96. doi: 10.1007/11866565_36.
This paper presents a novel approach for multi-organ (musculoskeletal system) automatic registration and segmentation from clinical MRI datasets, based on discrete deformable models (simplex meshes). We reduce the computational complexity using multi-resolution forces, multi-resolution hierarchical collision handling and large simulation time steps (implicit integration scheme), allowing real-time user control and cost-efficient segmentation. Radial forces and topological constraints (attachments) are applied to regularize the segmentation process. Based on a medial axis constrained approximation, we efficiently characterize shapes and deformations. We validate our methods for the hip joint and the thigh (20 muscles, 4 bones) on 4 datasets: average error = 1.5 mm, computation time = 15 min.
本文提出了一种基于离散可变形模型（单纯形网格）从临床MRI数据集中进行多器官（肌肉骨骼系统）自动配准和分割的新方法。我们使用多分辨率力、多分辨率分层碰撞处理和大模拟时间步长（隐式积分方案）来降低计算复杂度，从而实现实时用户控制和经济高效的分割。应用径向力和拓扑约束（附着）来规范分割过程。基于中轴约束近似，我们有效地表征形状和变形。我们在4个数据集上对髋关节和大腿（20块肌肉、4块骨头）验证了我们的方法：平均误差 = 1.5毫米，计算时间 = 15分钟。
