Towards 3D Semantic Image Synthesis for Medical Imaging

• URL: http://arxiv.org/abs/2507.00206v1
• Date: Mon, 30 Jun 2025 19:18:06 GMT
• Title: Towards 3D Semantic Image Synthesis for Medical Imaging
• Authors: Wenwu Tang, Khaled Seyam, Bin Yang,
• Abstract summary: Our study proposes the Med-LSDM (Latent Semantic Diffusion Model), which operates directly in the 3D domain.<n>Unlike many existing methods that focus on generating 2D slices, Med-LSDM is designed specifically for 3D semantic image synthesis.<n>Our approach demonstrates strong performance in 3D semantic medical image synthesis, achieving a 3D-FID score of 0.0054 on the conditional Duke Breast dataset.
• Score: 3.1265626879839923
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the medical domain, acquiring large datasets is challenging due to both accessibility issues and stringent privacy regulations. Consequently, data availability and privacy protection are major obstacles to applying machine learning in medical imaging. To address this, our study proposes the Med-LSDM (Latent Semantic Diffusion Model), which operates directly in the 3D domain and leverages de-identified semantic maps to generate synthetic data as a method of privacy preservation and data augmentation. Unlike many existing methods that focus on generating 2D slices, Med-LSDM is designed specifically for 3D semantic image synthesis, making it well-suited for applications requiring full volumetric data. Med-LSDM incorporates a guiding mechanism that controls the 3D image generation process by applying a diffusion model within the latent space of a pre-trained VQ-GAN. By operating in the compressed latent space, the model significantly reduces computational complexity while still preserving critical 3D spatial details. Our approach demonstrates strong performance in 3D semantic medical image synthesis, achieving a 3D-FID score of 0.0054 on the conditional Duke Breast dataset and similar Dice scores (0.70964) to those of real images (0.71496). These results demonstrate that the synthetic data from our model have a small domain gap with real data and are useful for data augmentation.

Related papers

• Towards Scalable Spatial Intelligence via 2D-to-3D Data Lifting [64.64738535860351]
  We present a scalable pipeline that converts single-view images into comprehensive, scale- and appearance-realistic 3D representations.<n>Our method bridges the gap between the vast repository of imagery and the increasing demand for spatial scene understanding.<n>By automatically generating authentic, scale-aware 3D data from images, we significantly reduce data collection costs and open new avenues for advancing spatial intelligence.
  arXiv  Detail & Related papers  (2025-07-24T14:53:26Z)
• 3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes [2.8498944632323755]
  We propose a novel slice-based latent diffusion architecture to address the complexities of volumetric data generation. This approach extends the joint distribution modeling of medical images and their associated masks, allowing a simultaneous generation of both under data-scarce regimes. Our architecture can be conditioned by tumor characteristics, including size, shape, and relative position, thereby providing a diverse range of tumor variations.
  arXiv  Detail & Related papers  (2024-06-08T09:53:45Z)
• Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation [68.60747298865394]
  We propose a new cross-dimensional SSL framework based on a pseudo-3D transformation (CDSSL-P3D) Specifically, we introduce an image transformation based on the im2col algorithm, which converts 2D images into a format consistent with 3D data. This transformation enables seamless integration of 2D and 3D data, and facilitates cross-dimensional self-supervised learning for 3D medical image analysis.
  arXiv  Detail & Related papers  (2024-06-03T02:57:25Z)
• Generative Enhancement for 3D Medical Images [74.17066529847546]
  We propose GEM-3D, a novel generative approach to the synthesis of 3D medical images. Our method begins with a 2D slice, noted as the informed slice to serve the patient prior, and propagates the generation process using a 3D segmentation mask. By decomposing the 3D medical images into masks and patient prior information, GEM-3D offers a flexible yet effective solution for generating versatile 3D images.
  arXiv  Detail & Related papers  (2024-03-19T15:57:04Z)
• Three-dimensional Bone Image Synthesis with Generative Adversarial Networks [2.499907423888049]
  This work demonstrates that three-dimensional generative adversarial networks (GANs) can be efficiently trained to generate high-resolution medical volumes. GAN inversion is successfully implemented for the three-dimensional setting and used for extensive research on model interpretability.
  arXiv  Detail & Related papers  (2023-10-26T08:08:17Z)
• 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation [52.699139151447945]
  We propose a novel adaptation method for transferring the segment anything model (SAM) from 2D to 3D for promptable medical image segmentation. Our model can outperform domain state-of-the-art medical image segmentation models on 3 out of 4 tasks, specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor, colon cancer segmentation, and achieve similar performance for liver tumor segmentation.
  arXiv  Detail & Related papers  (2023-06-23T12:09:52Z)
• Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis [0.0]
  We introduce Med-DDPM, a diffusion model designed for 3D semantic brain MRI synthesis. It effectively tackles data scarcity and privacy issues by integrating semantic conditioning. It generates diverse, coherent images with high visual fidelity.
  arXiv  Detail & Related papers  (2023-05-29T04:14:38Z)
• Medical Diffusion -- Denoising Diffusion Probabilistic Models for 3D Medical Image Generation [0.6486409713123691]
  We show that diffusion probabilistic models can synthesize high quality medical imaging data. We provide quantitative measurements of their performance through a reader study with two medical experts. We demonstrate that synthetic images can be used in a self-supervised pre-training and improve the performance of breast segmentation models when data is scarce.
  arXiv  Detail & Related papers  (2022-11-07T08:37:48Z)
• RiCS: A 2D Self-Occlusion Map for Harmonizing Volumetric Objects [68.85305626324694]
  Ray-marching in Camera Space (RiCS) is a new method to represent the self-occlusions of foreground objects in 3D into a 2D self-occlusion map. We show that our representation map not only allows us to enhance the image quality but also to model temporally coherent complex shadow effects.
  arXiv  Detail & Related papers  (2022-05-14T05:35:35Z)
• Fader Networks for domain adaptation on fMRI: ABIDE-II study [68.5481471934606]
  We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
  arXiv  Detail & Related papers  (2020-10-14T16:50:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.