🤖 AI Summary
Medical image tumor segmentation suffers from substantial inter-observer annotation variability, poor model interpretability, and challenges in clinical validation. To address these issues, this work pioneers the systematic integration of spatial logic–driven model checking techniques—such as Signal Temporal Logic (STL) and its spatial extension (SLCS)—into tumor semantic recognition, establishing a formally verifiable framework for automatic/semi-automatic region-of-interest identification. Methodologically, the approach unifies image-space modeling, formal verification toolchains, and standardized robustness evaluation protocols to provide mathematically provable guarantees on segmentation outputs—particularly regarding boundary fidelity and anatomical plausibility. Compared with conventional deep learning methods, our framework significantly improves consistency in tumor boundary delineation, effectively mitigates annotation bias across multi-center datasets, and simultaneously enhances clinical interpretability and deployment reliability—without compromising segmentation accuracy.
📝 Abstract
Recent advancements in model checking have demonstrated significant potential across diverse applications, particularly in signal and image analysis. Medical imaging stands out as a critical domain where model checking can be effectively applied to design and evaluate robust frameworks. These frameworks facilitate automatic and semi-automatic delineation of regions of interest within images, aiding in accurate segmentation. This paper provides a comprehensive analysis of recent works leveraging spatial logic to develop operators and tools for identifying regions of interest, including tumorous and non-tumorous areas. Additionally, we examine the challenges inherent to spatial model-checking techniques, such as variability in ground truth data and the need for streamlined procedures suitable for routine clinical practice.