Model Checking in Medical Imaging for Tumor Detection and Segmentation
This work addresses the problem of improving tumor detection and segmentation in medical imaging for clinical applications, but it appears to be an incremental review and analysis rather than introducing new methods.
This paper analyzes how spatial logic and model checking can be applied to medical imaging for tumor detection and segmentation, examining recent works that develop operators and tools for identifying regions of interest. It also discusses challenges like ground truth variability and the need for clinical practicality.
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.