Integrating Background Knowledge in Medical Semantic Segmentation with Logic Tensor Networks

2025-09-26

AI Summary

Medical image semantic segmentation is critical for clinical decision support, yet existing deep learning models exhibit limited robustness to noise and artifacts and struggle to effectively incorporate domain-specific medical knowledge. To address this, we propose a neuro-symbolic fusion framework that integrates Logic Tensor Networks (LTNs) with SwinUNETR. Specifically, we encode first-order logical constraints—such as hippocampal shape priors and anatomical relationships—into differentiable loss terms, seamlessly embedding symbolic knowledge into an end-to-end segmentation pipeline. The method significantly enhances model robustness and segmentation accuracy under data-scarce conditions. Extensive experiments on brain MRI datasets demonstrate that our approach outperforms state-of-the-art baselines in Dice score and other key metrics, with particularly pronounced gains in low-shot settings. These results validate the effectiveness and generalizability of logic-guided supervision for medical image segmentation.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Computer Vision: Segmentation
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Semantic segmentation is a fundamental task in medical image analysis, aiding medical decision-making by helping radiologists distinguish objects in an image. Research in this field has been driven by deep learning applications, which have the potential to scale these systems even in the presence of noise and artifacts. However, these systems are not yet perfected. We argue that performance can be improved by incorporating common medical knowledge into the segmentation model's loss function. To this end, we introduce Logic Tensor Networks (LTNs) to encode medical background knowledge using first-order logic (FOL) rules. The encoded rules span from constraints on the shape of the produced segmentation, to relationships between different segmented areas. We apply LTNs in an end-to-end framework with a SwinUNETR for semantic segmentation. We evaluate our method on the task of segmenting the hippocampus in brain MRI scans. Our experiments show that LTNs improve the baseline segmentation performance, especially when training data is scarce. Despite being in its preliminary stages, we argue that neurosymbolic methods are general enough to be adapted and applied to other medical semantic segmentation tasks.

Problem

• Integrating medical background knowledge into segmentation models
• Improving segmentation accuracy with logical constraints
• Addressing performance issues when training data is scarce

Innovation

• Integrating medical knowledge via Logic Tensor Networks
• Encoding first-order logic rules into loss function
• Combining LTNs with SwinUNETR for segmentation