生成符合解剖结构的肺部异常纹理，提升胸部X光图异常检测真实感
ART-ASyn: Anatomy-aware Realistic Texture-based Anomaly Synthesis Framework for Chest X-Rays
- 基于渐进二值阈值分割生成精准肺部区域，指导纹理异常合成
- 合成异常与真实病灶视觉相似，且配对像素级掩码用于显式监督
- 支持零样本异常分割，在未见数据集上表现良好，适合临床部署
无监督异常检测旨在无需像素级标注的情况下识别异常。基于合成异常的方法能够引入可控的不规则性并提供已知掩码，从而在训练中实现显式监督。然而，现有方法生成的合成异常常与真实病理模式视觉差异明显，且忽略解剖结构。本文提出一种新的解剖结构感知、基于纹理的胸部X光异常合成框架（ART-ASyn），利用我们提出的渐进二值阈值分割方法（PBTSeg）进行肺部分割，生成符合解剖结构的真实肺部阴影异常。每张正常图像均生成对应的合成异常及其精确像素级异常掩码，实现显式分割监督。相比以往仅限于单类分类的工作，ART-ASyn进一步评估了零样本异常分割能力，在未见过的数据集上无需目标域标注即表现出良好泛化性。代码已公开于 https://github.com/angelacao-hub/ART-ASyn。
原文摘要 · Abstract (English)
Unsupervised anomaly detection aims to identify anomalies without pixel-level annotations. Synthetic anomaly-based methods exhibit a unique capacity to introduce controllable irregularities with known masks, enabling explicit supervision during training. However, existing methods often produce synthetic anomalies that are visually distinct from real pathological patterns and ignore anatomical structure. This paper presents a novel Anatomy-aware Realistic Texture-based Anomaly Synthesis framework (ART-ASyn) for chest X-rays that generates realistic and anatomically consistent lung opacity related anomalies using texture-based augmentation guided by our proposed Progressive Binary Thresholding Segmentation method (PBTSeg) for lung segmentation. The generated paired samples of synthetic anomalies and their corresponding precise pixel-level anomaly mask for each normal sample enable explicit segmentation supervision. In contrast to prior work limited to one-class classification, ART-ASyn is further evaluated for zero-shot anomaly segmentation, demonstrating generalizability on an unseen dataset without target-domain annotations. Code availability is available at https://github.com/angelacao-hub/ART-ASyn.
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