Mechouche Ammar, Golbreich Christine, Morandi Xavier, Gibaud Bernard
Unit/Project VisAGeS U746, INSERM/INRIA/CNRS/ University of Rennes 1, Rennes, France.
AMIA Annu Symp Proc. 2008 Nov 6;2008:460-4.
This paper describes a hybrid system for annotating anatomical structures in brain Magnetic Resonance Images. The system involves both numerical knowledge from an atlas and symbolic knowledge represented in a rule-extended ontology, written in standard web languages, and symbolic constraints. The system combines this knowledge with graphical data automatically extracted from the images. The annotations of the parts of sulci and of gyri located in a region of interest selected by the user are obtained with a reasoning based on a Constraint Satisfaction Problem solving combined with Description Logics inference services. The first results obtained with both normal and pathological data are promising.
本文描述了一种用于标注脑磁共振图像中解剖结构的混合系统。该系统涉及来自图谱的数值知识以及用标准网络语言编写的规则扩展本体中表示的符号知识和符号约束。该系统将这些知识与从图像中自动提取的图形数据相结合。通过基于约束满足问题求解与描述逻辑推理服务相结合的推理，获得用户选择的感兴趣区域中脑沟和脑回部分的标注。使用正常和病理数据获得的初步结果很有前景。
