3D Knowledge-Based Segmentation Using Pose-Invariant Higher-Order Graphs

摘要：

Segmentation is a fundamental problem in medical image analysis. The use of prior knowledge is often considered to address the ill-posedness of the process. Such a process consists in bringing all training examples in the same reference pose, and then building statistics. During inference, pose parameters are usually estimated first, and then one seeks a compromise between data-attraction and model-fitness with the prior model. In this paper, we propose a novel higher-order Markov Random Field (MRF) model to encode pose-invariant priors and perform 3D segmentation of challenging data. The approach encodes data support in the singleton terms that are obtained using machine learning, and prior constraints in the higher-order terms. A dual-decomposition-based inference method is used to recover the optimal solution. Promising results on challenging data involving segmentation of tissue classes of the human skeletal muscle demonstrate the potentials of the method.

作者: Chaohui Wang Olivier Teboul Fabrice Michel Salma Essafi Nikos Paragios

作者单位: Laboratoire MAS, Ecole Centrale de Paris, FranceEquipe GALEN, INRIA Saclay - Ile de France, Orsay, F Laboratoire MAS, Ecole Centrale de Paris, France Microsoft France Laboratoire MAS, Ecole Centrale de Paris, France Equipe GALEN, INRIA Saclay - Ile de France, Orsay,

会议类型: 国际会议

会议名称: The 13th International Conference on Medical Image Computing and Computer-Assisted Intervention(第13届医学影像计算与计算机辅助介入国际会议 MICCAI 2010)

会议地点: 北京

会议语种:英文

页码: 189–196

在线出版日期: 2010-09-01（万方平台首次上网日期，不代表论文的发表时间）