用空间逻辑提升肿瘤检测与分割的可靠性
Model Checking in Medical Imaging for Tumor Detection and Segmentation
- 基于空间逻辑构建识别肿瘤区域的检测算子
- 支持自动与半自动勾画病灶区域，提升分割精度
- 适合医学影像分析场景，尤其关注临床可用性
近期模型检查技术在信号与图像分析中展现出巨大潜力。医学影像是其关键应用领域，可用于设计和评估稳健的框架，实现图像中感兴趣区域的自动或半自动勾画，辅助精准分割。本文系统分析了近年利用空间逻辑开发的算子与工具，用于识别包括肿瘤和非肿瘤区域在内的感兴趣区。同时，探讨了空间模型检查技术面临挑战，如标注数据的不一致性以及需满足临床日常使用所需的简化流程要求。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。