# Longitudinal Variability Analysis on Low-dose Abdominal CT with Deep Learning-based Segmentation

arXiv: https://arxiv.org/abs/2209.14217 · HTML: https://mlsanity.com/paper/2209.14217/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation](https://mlsanity.com/paper/1904.06346/) (2019)

## Built on by (most cited first, 5 of 5)

- [Enhancing Single-Slice Segmentation with 3D-to-2D Unpaired Scan Distillation](https://mlsanity.com/paper/2406.12254/) (2024)
- [Single Slice Thigh CT Muscle Group Segmentation with Domain Adaptation and Self-Training](https://mlsanity.com/paper/2212.00059/) (2022)
- [Deep conditional generative models for longitudinal single-slice abdominal computed tomography harmonization](https://mlsanity.com/paper/2309.09392/) (2023)
- [Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential](https://mlsanity.com/paper/2501.18834/) (2025)
- [Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration](https://mlsanity.com/paper/2309.04071/) (2023)

## Abstract (arXiv)

Metabolic health is increasingly implicated as a risk factor across conditions from cardiology to neurology, and efficiency assessment of body composition is critical to quantitatively characterizing these relationships. 2D low dose single slice computed tomography (CT) provides a high resolution, quantitative tissue map, albeit with a limited field of view. Although numerous potential analyses have been proposed in quantifying image context, there has been no comprehensive study for low-dose single slice CT longitudinal variability with automated segmentation. We studied a total of 1816 slices from 1469 subjects of Baltimore Longitudinal Study on Aging (BLSA) abdominal dataset using supervised deep learning-based segmentation and unsupervised clustering method. 300 out of 1469 subjects that have two year gap in their first two scans were pick out to evaluate longitudinal variability with measurements including intraclass correlation coefficient (ICC) and coefficient of variation (CV) in terms of tissues/organs size and mean intensity. We showed that our segmentation methods are stable in longitudinal settings with Dice ranged from 0.821 to 0.962 for thirteen target abdominal tissues structures. We observed high variability in most organ with ICC<0.5, low variability in the area of muscle, abdominal wall, fat and body mask with average ICC>0.8. We found that the variability in organ is highly related to the cross-sectional position of the 2D slice. Our efforts pave quantitative exploration and quality control to reduce uncertainties in longitudinal analysis.