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

AATCT-IDS: A Benchmark Abdominal Adipose Tissue CT Image Dataset for Image Denoising, Semantic Segmentation, and Radiomics Evaluation