Diff-Lung：用于增强肺部CT扫描中病理组织分割的基于扩散的纹理合成
Diff-Lung: Diffusion-Based Texture Synthesis for Enhanced Pathological Tissue Segmentation in Lung CT Scans
AI总结：
针对肺部CT中健康与病理组织类别不平衡问题，提出Diff-Lung扩散模型数据增强方法，生成保留形状与纹理细节的病理组织块，提升各类病理模式尤其是少见类型的分割准确率。
摘要：
准确量化肺部病理模式（纤维化、磨玻璃影、肺气肿、实变）的范围是间质性肺疾病诊断和随访的先决条件。然而，由于健康组织与病理组织之间存在显著的类别不平衡，分割颇具挑战性。本文通过在训练AI模型期间应用扩散模型进行数据增强来解决这一问题。我们的方法生成合成病理组织块，同时保留每种组织类型特有的基本形状特征和复杂细节。该方法通过增加训练数据中代表性不足类别的出现频率来增强分割过程。我们证明，基于扩散的增强技术提高了所有病理组织类型的分割准确率，尤其是对较少见的模式。这一进展有助于更可靠地自动化分析肺部CT扫描，可能改善临床决策和患者预后。
英文摘要：
Accurate quantification of the extent of lung pathological patterns (fibrosis, ground-glass opacity, emphysema, consolidation) is prerequisite for diagnosis and follow-up of interstitial lung diseases. However, segmentation is challenging due to the significant class imbalance between healthy and pathological tissues. This paper addresses this issue by leveraging a diffusion model for data augmentation applied during training an AI model. Our approach generates synthetic pathological tissue patches while preserving essential shape characteristics and intricate details specific to each tissue type. This method enhances the segmentation process by increasing the occurence of underrepresented classes in the training data. We demonstrate that our diffusion-based augmentation technique improves segmentation accuracy across all pathological tissue types, particularly for the less common patterns. This advancement contributes to more reliable automated analysis of lung CT scans, potentially improving clinical decision-making and patient outcomes
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
