First Order Logic with Fuzzy Semantics for Describing and Recognizing Nerves in Medical Images
This work addresses the challenge of imprecise anatomical descriptions for surgeons, but it is incremental as it applies existing logical methods to a specific medical domain.
The authors tackled the problem of describing and recognizing nerves in medical images by formalizing anatomical knowledge using first-order logic with fuzzy semantics, resulting in a spatial reasoning algorithm for segmentation and recognition, demonstrated on pelvic nerves in pediatric imaging to aid surgical planning.
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.