# Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models

arXiv: https://arxiv.org/abs/2209.14467 · HTML: https://mlsanity.com/paper/2209.14467/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Autoencoding beyond pixels using a learned similarity metric](https://mlsanity.com/paper/1512.09300/) (2015)
- [Learning from Irregularly-Sampled Time Series: A Missing Data Perspective](https://mlsanity.com/paper/2008.07599/) (2020)
- [EndoL2H: Deep Super-Resolution for Capsule Endoscopy](https://mlsanity.com/paper/2002.05459/) (2020)
- [Unsupervised Video Prediction from a Single Frame by Estimating 3D Dynamic Scene Structure](https://mlsanity.com/paper/2106.09051/) (2021)
- [Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective](https://mlsanity.com/paper/2107.11882/) (2021)

## Built on by (most cited first, 7 of 7)

- [Full-dose Whole-body PET Synthesis from Low-dose PET Using High-efficiency Denoising Diffusion Probabilistic Model: PET Consistency Model](https://mlsanity.com/paper/2308.13072/) (2023)
- [Spectrum AUC Difference (SAUCD): Human-aligned 3D Shape Evaluation](https://mlsanity.com/paper/2403.01619/) (2024)
- [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)
- [Robust Body Composition Analysis by Generating 3D CT Volumes from Limited 2D Slices](https://mlsanity.com/paper/2501.13071/) (2025)
- [Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration](https://mlsanity.com/paper/2309.04071/) (2023)

## Abstract (arXiv)

2D low-dose single-slice abdominal computed tomography (CT) slice enables direct measurements of body composition, which are critical to quantitatively characterizing health relationships on aging. However, longitudinal analysis of body composition changes using 2D abdominal slices is challenging due to positional variance between longitudinal slices acquired in different years. To reduce the positional variance, we extend the conditional generative models to our C-SliceGen that takes an arbitrary axial slice in the abdominal region as the condition and generates a defined vertebral level slice by estimating the structural changes in the latent space. Experiments on 1170 subjects from an in-house dataset and 50 subjects from BTCV MICCAI Challenge 2015 show that our model can generate high quality images in terms of realism and similarity. External experiments on 20 subjects from the Baltimore Longitudinal Study of Aging (BLSA) dataset that contains longitudinal single abdominal slices validate that our method can harmonize the slice positional variance in terms of muscle and visceral fat area. Our approach provides a promising direction of mapping slices from different vertebral levels to a target slice to reduce positional variance for single slice longitudinal analysis. The source code is available at: https://github.com/MASILab/C-SliceGen.