用生成模型提升肺纤维化弱监督分割精度，减少人工标注量
Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generation
- 基于扩散模型生成不同纤维化程度的健康肺部图像
- 伪标签准确率显著优于现有弱监督方法
- 适合医学图像分割初学者与需要降本增效的研究者
肺纤维化疾病（FLD）是一种以肺组织硬化和瘢痕形成为特征的严重疾病，导致呼吸功能下降。高分辨率计算机断层扫描（HRCT）对诊断和监测至关重要，但纤维化表现为不规则、弥散性病灶且边界模糊，造成医生间判读差异大且手动标注耗时。为此，我们提出DiffSeg，一种新型弱监督语义分割（WSSS）方法，仅使用图像级标签即可生成像素级纤维化分割图，大幅降低细粒度标注需求。此外，该方法引入基于扩散的生成模型，从健康切片合成具有不同程度纤维化的HRCT图像，生成带纤维化注入的切片及其对应的病灶位置。实验表明，该方法显著提升了现有WSSS方法生成伪标签的准确性，极大降低了人工标注复杂度，并增强了分割结果的一致性。
原文摘要 · Abstract (English)
Fibrotic Lung Disease (FLD) is a severe condition marked by lung stiffening and scarring, leading to respiratory decline. High-resolution computed tomography (HRCT) is critical for diagnosing and monitoring FLD; however, fibrosis appears as irregular, diffuse patterns with unclear boundaries, leading to high inter-observer variability and time-intensive manual annotation. To tackle this challenge, we propose DiffSeg, a novel weakly supervised semantic segmentation (WSSS) method that uses image-level annotations to generate pixel-level fibrosis segmentation, reducing the need for fine-grained manual labeling. Additionally, our DiffSeg incorporates a diffusion-based generative model to synthesize HRCT images with different levels of fibrosis from healthy slices, enabling the generation of the fibrosis-injected slices and their paired fibrosis location. Experiments indicate that our method significantly improves the accuracy of pseudo masks generated by existing WSSS methods, greatly reducing the complexity of manual labeling and enhancing the consistency of the generated masks.
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