用模糊逻辑描述神经轨迹，提升医学影像中神经识别精度。
First Order Logic with Fuzzy Semantics for Describing and Recognizing Nerves in Medical Images
- 结合一阶逻辑与模糊语义建模神经解剖描述
- 在儿童盆腔影像中实现神经分割与识别，支持术前规划
- 适合医学图像分析与智能手术辅助研究者
本文针对医学影像中纤维束（特别是神经）的描述与识别问题，基于纤维轨迹的解剖学描述提出一种逻辑形式化方法。由于解剖学教材中对神经的描述具有固有模糊性，我们引入模糊语义与一阶逻辑相结合的框架。定义了一种语言，用于表示空间实体、实体间关系及量化表达；公式即为自然语言描述的形式化表达。语义通过具体域中的模糊表示和关系满足度给出。基于此形式化，提出一种空间推理算法，用于从解剖与弥散磁共振图像中分割并识别神经，以儿科盆腔神经为例，帮助外科医生进行术前规划。
原文摘要 · Abstract (English)
This article deals with the description and recognition of fiber bundles, in particular nerves, in medical images, based on the anatomical description of the fiber trajectories. To this end, we propose a logical formalization of this anatomical knowledge. The intrinsically imprecise description of nerves, as found in anatomical textbooks, leads us to propose fuzzy semantics combined with first-order logic. We define a language representing spatial entities, relations between these entities and quantifiers. A formula in this language is then a formalization of the natural language description. The semantics are given by fuzzy representations in a concrete domain and satisfaction degrees of relations. Based on this formalization, a spatial reasoning algorithm is proposed for segmentation and recognition of nerves from anatomical and diffusion magnetic resonance images, which is illustrated on pelvic nerves in pediatric imaging, enabling surgeons to plan surgery.
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