  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}.
