用图像重建+局部相似性评分，精准定位肺部和脑部病变。
AREPAS: Anomaly Detection in Fine-Grained Anatomy with Reconstruction-Based Semantic Patch-Scoring
- 先重建正常图像，再比对局部区域差异来定位异常。
- 在胸部CT和脑部MRI上，分割精度提升1.9%至4.4%。
- 适合需要高精度病变定位的医学影像分析场景。
早期发现新发疾病、评估病灶严重程度、区分不同病情以及自动化筛查，均体现了异常检测（AD）与无监督分割在医学中的广泛应用与重要性。肺部解剖等细微组织变异是现有生成式异常检测方法面临的主要挑战。本文提出一种新型生成式异常检测方法，包含无异常图像重建与观察图像与生成图像对之间的局部块相似性评分，实现精确异常定位。我们在胸部计算机断层扫描（CT）上验证该方法对感染性病灶的检测与分割能力，并评估其在T1加权脑部磁共振成像上的缺血性中风病灶分割任务中的泛化性能。结果表明，在胸部CT与脑部MRI上，像素级异常分割效果均优于现有最先进重建方法，相对DICE分数分别提升1.9%和4.4%。
原文摘要 · Abstract (English)
Early detection of newly emerging diseases, lesion severity assessment, differentiation of medical conditions and automated screening are examples for the wide applicability and importance of anomaly detection (AD) and unsupervised segmentation in medicine. Normal fine-grained tissue variability such as present in pulmonary anatomy is a major challenge for existing generative AD methods. Here, we propose a novel generative AD approach addressing this issue. It consists of an image-to-image translation for anomaly-free reconstruction and a subsequent patch similarity scoring between observed and generated image-pairs for precise anomaly localization. We validate the new method on chest computed tomography (CT) scans for the detection and segmentation of infectious disease lesions. To assess generalizability, we evaluate the method on an ischemic stroke lesion segmentation task in T1-weighted brain MRI. Results show improved pixel-level anomaly segmentation in both chest CTs and brain MRIs, with relative DICE score improvements of +1.9% and +4.4%, respectively, compared to other state-of-the-art reconstruction-based methods.
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