Deep conditional generative models for longitudinal single-slice abdominal computed tomography harmonization

TL;DR

This paper introduces C-SliceGen, a deep generative model that harmonizes longitudinal abdominal CT slices by generating consistent slices at specific vertebral levels, improving analysis of body composition changes over time.

Contribution

The paper presents a novel deep conditional generative model for harmonizing longitudinal abdominal CT slices by estimating structural changes in the latent space.

Findings

Generated images are realistic and similar to real slices.

The method effectively harmonizes slice positional variance.

Improves longitudinal analysis of visceral fat area.

Abstract

Two-dimensional single-slice abdominal computed tomography (CT) provides a detailed tissue map with high resolution allowing quantitative characterization of relationships between health conditions and aging. However, longitudinal analysis of body composition changes using these scans is difficult due to positional variation between slices acquired in different years, which leading to different organs/tissues captured. To address this issue, we propose C-SliceGen, which takes an arbitrary axial slice in the abdominal region as a condition and generates a pre-defined vertebral level slice by estimating structural changes in the latent space. Our experiments on 2608 volumetric CT data from two in-house datasets and 50 subjects from the 2015 Multi-Atlas Abdomen Labeling Challenge dataset (BTCV) Challenge demonstrate that our model can generate high-quality images that are realistic and similar. We further evaluate our method's capability to harmonize longitudinal positional variation on 1033 subjects from the Baltimore Longitudinal Study of Aging (BLSA) dataset, which contains longitudinal single abdominal slices, and confirmed that our method can harmonize the slice positional variance in terms of visceral fat area. This approach provides a promising direction for mapping slices from different vertebral levels to a target slice and reducing positional variance for single-slice longitudinal analysis. The source code is available at: https://github.com/MASILab/C-SliceGen.