First Order Logic with Fuzzy Semantics for Describing and Recognizing Nerves in Medical Images

2025-04-30

AI Summary

Accurate segmentation of neural tracts—particularly pediatric pelvic nerves—is hindered by inherent ambiguity in anatomical descriptions, leading to poor delineation in medical imaging. Method: We propose a first-order logic framework integrating fuzzy semantics, the first to formally encode uncertainty from natural-language anatomical knowledge into a computable spatial logic model capable of representing spatial entities, relations, and quantifiers. Our approach combines diffusion MRI (dMRI) processing with a logic-satisfaction-driven spatial reasoning algorithm to achieve end-to-end mapping from imprecise anatomical descriptions to pixel-level segmentation. Contribution/Results: Evaluated on pediatric pelvic dMRI data, our method significantly improves automated tract segmentation accuracy and anatomical interpretability. It establishes a novel paradigm for preoperative planning that jointly ensures computational precision and clinical intelligibility.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Segmentation
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Describing and recognizing nerves in medical images
• Formalizing anatomical knowledge with fuzzy logic
• Segmenting nerves using spatial reasoning algorithms

Innovation

• Fuzzy semantics combined with first-order logic
• Language for spatial entities and relations
• Spatial reasoning algorithm for nerve recognition