Methods: In this study, a benchmark \emph{Abdominal Adipose Tissue CT Image Dataset} (AATTCT-IDS) containing 300 subjects is prepared and published. AATTCT-IDS publics 13,732 raw CT slices, and the researchers individually annotate the subcutaneous and visceral adipose tissue regions of 3,213 of those slices that have the same slice distance to validate denoising methods, train semantic segmentation models, and study radiomics. For different tasks, this paper compares and analyzes the performance of various methods on AATTCT-IDS by combining the visualization results and evaluation data. Thus, verify the research potential of this data set in the above three types of tasks. Results: In the comparative study of image denoising, algorithms using a smoothing strategy suppress mixed noise at the expense of image details and obtain better evaluation data. Methods such as BM3D preserve the original image structure better, although the evaluation data are slightly lower. The results show significant differences among them. In the comparative study of semantic segmentation of abdominal adipose tissue, the segmentation results of adipose tissue by each model show different structural characteristics. Among them, BiSeNet obtains segmentation results only slightly inferior to U-Net with the shortest training time and effectively separates small and isolated adipose tissue. In addition, the radiomics study based on AATTCT-IDS reveals three adipose distributions in the subject population. Conclusion: AATTCT-IDS contains the ground truth of adipose tissue regions in abdominal CT slices. This open-source dataset can attract researchers to explore the multi-dimensional characteristics of abdominal adipose tissue and thus help physicians and patients in clinical practice. AATCT-IDS is freely published for non-commercial purpose at: \url{https://figshare.com/articles/dataset/AATTCT-IDS/23807256}.

翻译：方法：本研究制备并发布了一个包含300名受试者的基准腹部脂肪组织CT图像数据集（AATTCT-IDS）。该数据集公开了13,732张原始CT切片，其中研究人员对具有相同切片间距的3,213张切片进行了皮下和内脏脂肪组织区域的独立标注，用于验证去噪方法、训练语义分割模型及开展影像组学研究。针对不同任务，本文结合可视化结果与评估数据，比较分析了多种方法在AATTCT-IDS上的性能表现，从而验证该数据集在上述三类任务中的研究潜力。结果：在图像去噪对比研究中，采用平滑策略的算法以牺牲图像细节为代价抑制混合噪声，获得更优的评估数据；而BM3D等方法虽评估指标略低，但能更好地保留原始图像结构。结果显示两者存在显著差异。在腹部脂肪组织语义分割对比研究中，各模型的分割结果呈现不同的结构特征。其中，BiSeNet以最短训练时间获得仅略逊于U-Net的分割结果，并能有效分离孤立的小块脂肪组织。此外，基于AATTCT-IDS的影像组学研究揭示了受试者群体中的三种脂肪分布模式。结论：AATTCT-IDS包含腹部CT切片脂肪组织区域的真实标注。该开源数据集可吸引研究者探索腹部脂肪组织的多维特征，从而在临床实践中辅助医生与患者。AATTCT-IDS已免费公开发布于非商业用途网址：\url{https://figshare.com/articles/dataset/AATTCT-IDS/23807256}。