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首页> 外文会议>International Workshop on Computer Vision for Biomedical Image Applications(CVBIA 2005); 20051021; Beijing(CN) >Non-rigid Registration for Colorectal Cancer MR Images
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Non-rigid Registration for Colorectal Cancer MR Images

机译：大肠癌MR图像的非刚性配准

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We are developing a system for patient management in col-orectal cancer, in which the need for segmentation and non-rigid registration of pre- and post-therapy images arises. Several methods for non-rigid registration have been proposed, all of which embody a 'generic' algorithm to solve registration, largely irrespective both of the kinds of images and of the application. We have evaluated several of these algorithms for this application and find their performance unsuitable for aligning pre- and post- therapy colorectal images. This leads us to identify some of the implicit assumptions and fundamental limitations of these algorithms. None of the currently available algorithms take into account the issue of scale salience and more importantly, none of the algorithms "know" enough about colorectal MRI to focus their attention for registration on those parts of the image that are clinically important. Based on this analysis, we propose a way in which we can perform registration by mobilizing the knowledge of the particular application, for example the prior shape knowledge that we have within the colorectal images as well as knowledge of the large scale non-rigid changes due to therapy.
机译：我们正在开发一种用于结肠直肠癌患者管理的系统，其中需要对治疗前和治疗后图像进行分割和非刚性注册。已经提出了几种用于非刚性配准的方法，所有这些方法都体现了一种“通用”算法来解决配准，这在很大程度上与图像的种类和应用无关。我们已经针对该应用评估了其中几种算法，发现它们的性能不适合对齐治疗前后的结直肠图像。这使我们确定了这些算法的一些隐含假设和基本限制。当前没有可用的算法考虑比例尺显着性的问题，更重要的是，没有一种算法对结直肠MRI有足够的“了解”，以将注意力集中在图像上具有临床重要性的部分上。在此分析的基础上，我们提出了一种方法，可以通过动员特定应用程序的知识来进行配准，例如，我们在结直肠图像中拥有的先验形状知识以及由于大范围非刚性变化而引起的知识治疗。

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