mlsanityData to 14 Oct 2026

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

arXiv 2308.08172 ↗First posted 16 Aug 2023eess.IV cs.CV cs.LGcited by 2

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Abstract (arXiv)

Methods: In this study, a benchmark 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: urlhttps://figshare.com/articles/dataset/AATTCT-IDS/23807256.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 21 of its 67 references that are in the map. First posted on arXiv 16 Aug 2023.

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21 of 67 references.

References · 21

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9 more
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Citing papers per year: 2025: 2.

2 papers.

  1. 2025How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?cited by 19
  2. 2025Attention GhostUNet++: Enhanced Segmentation of Adipose Tissue and Liver in CT Imagescited by 0