Dense deformation field estimation for atlas-based segmentation of pathological MR brain images

作     者：Cuadra, M. Bach De Craene, M. Duay, V. Macq, B. Pollo, C. Thiran, J. -Ph. 

作者机构：Ecole Polytech Fed Lausanne Signal Proc Inst ITS CH-1015 Lausanne Switzerland Catholic Univ Louvain Commun Lab B-1348 Louvain Belgium Univ Lausanne Hosp Dept Neurosurg CH-1011 Lausanne Switzerland 

出 版 物：《COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE》 (生物医学的计算机方法与程序)

年 卷 期：2006年第84卷第2-3期

页      面：66-75页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 1001[医学-基础医学(可授医学、理学学位)] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：Belgian Walloon Region, (EPI A320501R049F/415732) Geneva-Lausanne Universities foundations Leenaards and Louis-Jeantet École Polytechnique Fédérale de Lausanne, EPFL Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF, (205320-101621) Centre d'Imagerie BioMédicale, CIBM 

主　　题：atlas-based segmentation brain tumors MR imaging registration 

摘      要：Atlas registration is a recognized paradigm for the automatic segmentation of normal MR brain images. Unfortunately, atlas-based segmentation has been of limited use in presence of large space-occupying lesions. In fact, brain deformations induced by such lesions are added to normal anatomical variability and they may dramatically shift and deform anatomically or functionally important brain structures. In this work, we chose to focus on the problem of inter-subject registration of MR images with large tumors, inducing a significant shift of surrounding anatomical structures. First, a brief survey of the existing methods that have been proposed to deal with this problem is presented. This introduces the discussion about the requirements and desirable properties that we consider necessary to be fulfilled by a registration method in this context: To have a dense and smooth deformation field and a model of lesion growth, to model different deformability for some structures, to introduce more prior knowledge, and to use voxel-based features with a similarity measure robust to intensity differences. In a second part of this work, we propose a new approach that overcomes some of the main limitations of the existing techniques while complying with most of the desired requirements above. Our algorithm combines the mathematical framework for computing a variational flow proposed by Hermosillo et al. [G. Hermosillo, C. Chefd Hotel, O. Faugeras, A variational approach to multi-modal image matching, Tech. Rep., INRIA (February 2001).] with the radial lesion growth pattern presented by Bach et al. [M. Bach Cuadra, C. Pollo, A. Bardera, O. Cuisenaire, J.-G. Villemure, J.-Ph. Thiran, Atlas-based segmentation of pathological MR brain images using a model of lesion growth, IEEE Trans. Med. Imag. 23 (10) (2004) 1301-1314.]. Results on patients with a meningioma are visually assessed and compared to those obtained with the most similar method from the state-of-the-art. (c) 2006 Elsevi