Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processing
- URL: http://arxiv.org/abs/2303.15288v2
- Date: Thu, 12 Sep 2024 07:26:21 GMT
- Title: Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processing
- Authors: Florentin Bieder, Julia Wolleb, Alicia Durrer, Robin Sandkühler, Philippe C. Cattin,
- Abstract summary: We present a number of ways to reduce the resource consumption for 3D diffusion models.
The main contribution of this paper is the memory-efficient patch-based diffusion model.
While the proposed diffusion model can be applied to any image generation tasks, we evaluate the method on the tumor segmentation task of the BraTS 2020 dataset.
- Score: 0.9424565541639366
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Denoising diffusion models have recently achieved state-of-the-art performance in many image-generation tasks. They do, however, require a large amount of computational resources. This limits their application to medical tasks, where we often deal with large 3D volumes, like high-resolution three-dimensional data. In this work, we present a number of different ways to reduce the resource consumption for 3D diffusion models and apply them to a dataset of 3D images. The main contribution of this paper is the memory-efficient patch-based diffusion model \textit{PatchDDM}, which can be applied to the total volume during inference while the training is performed only on patches. While the proposed diffusion model can be applied to any image generation tasks, we evaluate the method on the tumor segmentation task of the BraTS2020 dataset and demonstrate that we can generate meaningful three-dimensional segmentations.
Related papers
- Introducing 3D Representation for Medical Image Volume-to-Volume Translation via Score Fusion [3.3559609260669303]
We present Score-Fusion, a novel volumetric translation model that effectively learns 3D representations by ensembling perpendicularly trained 2D diffusion models in score function space.
We show that Score-Fusion achieves superior accuracy and volumetric fidelity in 3D medical image super-resolution and modality translation.
arXiv Detail & Related papers (2025-01-13T15:54:21Z) - 3D MedDiffusion: A 3D Medical Diffusion Model for Controllable and High-quality Medical Image Generation [47.701856217173244]
3D Medical Diffusion (3D MedDiffusion) model for controllable, high-quality 3D medical image generation.
3D MedDiffusion incorporates a novel, highly efficient Patch-Volume Autoencoder that compresses medical images into latent space through patch-wise encoding.
We show that 3D MedDiffusion surpasses state-of-the-art methods in generative quality and exhibits strong generalizability across tasks such as sparse-view CT reconstruction, fast MRI reconstruction, and data augmentation.
arXiv Detail & Related papers (2024-12-17T16:25:40Z) - DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models [67.50989119438508]
We introduce DSplats, a novel method that directly denoises multiview images using Gaussian-based Reconstructors to produce realistic 3D assets.
Our experiments demonstrate that DSplats not only produces high-quality, spatially consistent outputs, but also sets a new standard in single-image to 3D reconstruction.
arXiv Detail & Related papers (2024-12-11T07:32:17Z) - Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes [2.8498944632323755]
We propose an end-to-end hybrid architecture for medical image segmentation.
We use Hamiltonian Variational Autoencoders (HVAE) and a discriminative regularization to improve the quality of generated images.
Our architecture operates on a slice-by-slice basis to segment 3D volumes, capitilizing on the richly augmented dataset.
arXiv Detail & Related papers (2024-06-17T15:42:08Z) - Blaze3DM: Marry Triplane Representation with Diffusion for 3D Medical Inverse Problem Solving [8.544098279063597]
We present Blaze3DM, a novel approach that enables fast and high-fidelity generation by integrating compact triplane neural field and powerful diffusion model.
In technique, Blaze3DM begins by optimizing data-dependent triplane embeddings and a shared decoder simultaneously, reconstructing each triplane back to the corresponding 3D volume.
Experiments on zero-shot 3D medical inverse problem solving, including sparse-view CT, limited-angle CT, compressed-sensing MRI, and MRI isotropic super-resolution, demonstrate that Blaze3DM not only achieves state-of-the-art performance but also markedly improves computational efficiency
arXiv Detail & Related papers (2024-05-24T06:07:27Z) - IT3D: Improved Text-to-3D Generation with Explicit View Synthesis [71.68595192524843]
This study presents a novel strategy that leverages explicitly synthesized multi-view images to address these issues.
Our approach involves the utilization of image-to-image pipelines, empowered by LDMs, to generate posed high-quality images.
For the incorporated discriminator, the synthesized multi-view images are considered real data, while the renderings of the optimized 3D models function as fake data.
arXiv Detail & Related papers (2023-08-22T14:39:17Z) - Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models [52.529394863331326]
We propose a novel approach using two perpendicular pre-trained 2D diffusion models to solve the 3D inverse problem.
Our method is highly effective for 3D medical image reconstruction tasks, including MRI Z-axis super-resolution, compressed sensing MRI, and sparse-view CT.
arXiv Detail & Related papers (2023-03-15T08:28:06Z) - Hierarchical Amortized Training for Memory-efficient High Resolution 3D
GAN [52.851990439671475]
We propose a novel end-to-end GAN architecture that can generate high-resolution 3D images.
We achieve this goal by using different configurations between training and inference.
Experiments on 3D thorax CT and brain MRI demonstrate that our approach outperforms state of the art in image generation.
arXiv Detail & Related papers (2020-08-05T02:33:04Z) - Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE [66.63629641650572]
We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices.
We also introduce a novel evaluation method for generated volumes that quantifies how well their segmentations match those of true brain anatomy.
arXiv Detail & Related papers (2020-07-09T13:23:15Z)
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.