Model Checking in Medical Imaging for Tumor Detection and Segmentation

TL;DR

This paper reviews how model checking, especially using spatial logic, can improve tumor detection and segmentation in medical imaging, highlighting recent advances, challenges, and potential for clinical application.

Contribution

It provides a comprehensive analysis of recent spatial model checking techniques applied to medical imaging for tumor delineation, emphasizing their potential and challenges.

Findings

Spatial logic-based operators enhance region identification.

Model checking frameworks support automatic tumor segmentation.

Challenges include data variability and clinical workflow integration.

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.