# Semantic Image Synthesis for Abdominal CT

arXiv: https://arxiv.org/abs/2312.06453 · HTML: https://mlsanity.com/paper/2312.06453/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 23 of its 27 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [Tackling the Generative Learning Trilemma with Denoising Diffusion GANs](https://mlsanity.com/paper/2112.07804/) (2021)
- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [Score-based diffusion models for accelerated MRI](https://mlsanity.com/paper/2110.05243/) (2021)
- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [Diffusion Models for Medical Anomaly Detection](https://mlsanity.com/paper/2203.04306/) (2022)
- [Diffusion Models for Implicit Image Segmentation Ensembles](https://mlsanity.com/paper/2112.03145/) (2021)
- [Unsupervised Medical Image Translation with Adversarial Diffusion Models](https://mlsanity.com/paper/2207.08208/) (2022)
- [Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks](https://mlsanity.com/paper/1807.10225/) (2018)
- [Semantic Image Synthesis via Diffusion Models](https://mlsanity.com/paper/2207.00050/) (2022)
- [You Only Need Adversarial Supervision for Semantic Image Synthesis](https://mlsanity.com/paper/2012.04781/) (2020)
- [Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network](https://mlsanity.com/paper/1806.05473/) (2018)
- [DiffuseMorph: Unsupervised Deformable Image Registration Using Diffusion Model](https://mlsanity.com/paper/2112.05149/) (2021)
- [Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks](https://mlsanity.com/paper/1805.11291/) (2018)
- [ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans](https://mlsanity.com/paper/1808.04500/) (2018)
- [High-Fidelity Image Synthesis from Pulmonary Nodule Lesion Maps using Semantic Diffusion Model](https://mlsanity.com/paper/2305.01138/) (2023)

## Built on by (most cited first, 10 of 10)

- [A Comprehensive Survey of Foundation Models in Medicine](https://mlsanity.com/paper/2406.10729/) (2024)
- [MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applications](https://mlsanity.com/paper/2410.15432/) (2024)
- [CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation](https://mlsanity.com/paper/2403.17770/) (2024)
- [A Comprehensive Survey on Diffusion Models and Their Applications](https://mlsanity.com/paper/2408.10207/) (2024)
- [Image Synthesis with Class-Aware Semantic Diffusion Models for Surgical Scene Segmentation](https://mlsanity.com/paper/2410.23962/) (2024)
- [GuideGen: A Text-Guided Framework for Paired Full-torso Anatomy and CT Volume Generation](https://mlsanity.com/paper/2403.07247/) (2024)
- [Appearance Matching Adapter for Exemplar-based Semantic Image Synthesis in-the-Wild](https://mlsanity.com/paper/2412.03150/) (2024)
- [ZECO: ZeroFusion Guided 3D MRI Conditional Generation](https://mlsanity.com/paper/2503.18246/) (2025)
- [AnatoMaskGAN: GNN-Driven Slice Feature Fusion and Noise Augmentation for Medical Semantic Image Synthesis](https://mlsanity.com/paper/2508.11375/) (2025)
- [MedSyn2: Flexible Control of 3D CT Generation via Text and Semantically-Defined Segmentation Prompts](https://mlsanity.com/paper/2606.00967/) (2026)

## Abstract (arXiv)

As a new emerging and promising type of generative models, diffusion models have proven to outperform Generative Adversarial Networks (GANs) in multiple tasks, including image synthesis. In this work, we explore semantic image synthesis for abdominal CT using conditional diffusion models, which can be used for downstream applications such as data augmentation. We systematically evaluated the performance of three diffusion models, as well as to other state-of-the-art GAN-based approaches, and studied the different conditioning scenarios for the semantic mask. Experimental results demonstrated that diffusion models were able to synthesize abdominal CT images with better quality. Additionally, encoding the mask and the input separately is more effective than na"ive concatenating.