3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes
- URL: http://arxiv.org/abs/2406.05421v1
- Date: Sat, 8 Jun 2024 09:53:45 GMT
- Title: 3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes
- Authors: Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan,
- Abstract summary: 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.
- Score: 2.8498944632323755
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Despite the increasing use of deep learning in medical image segmentation, the limited availability of annotated training data remains a major challenge due to the time-consuming data acquisition and privacy regulations. In the context of segmentation tasks, providing both medical images and their corresponding target masks is essential. However, conventional data augmentation approaches mainly focus on image synthesis. In this study, we propose a novel slice-based latent diffusion architecture designed to address the complexities of volumetric data generation in a slice-by-slice fashion. 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 approach mitigates the computational complexity and memory expensiveness typically associated with diffusion models. Furthermore, our architecture can be conditioned by tumor characteristics, including size, shape, and relative position, thereby providing a diverse range of tumor variations. Experiments on a segmentation task using the BRATS2022 confirm the effectiveness of the synthesized volumes and masks for data augmentation.
Related papers
- Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis [55.959002385347645]
Scaling by training on large datasets has been shown to enhance the quality and fidelity of image generation and manipulation with diffusion models.
Latent Drifting enables diffusion models to be conditioned for medical images fitted for the complex task of counterfactual image generation.
Our results demonstrate significant performance gains in various scenarios when combined with different fine-tuning schemes.
arXiv Detail & Related papers (2024-12-30T01:59:34Z) - Enhanced MRI Representation via Cross-series Masking [48.09478307927716]
Cross-Series Masking (CSM) Strategy for effectively learning MRI representation in a self-supervised manner.
Method achieves state-of-the-art performance on both public and in-house datasets.
arXiv Detail & Related papers (2024-12-10T10:32:09Z) - MRGen: Diffusion-based Controllable Data Engine for MRI Segmentation towards Unannotated Modalities [59.61465292965639]
This paper investigates a new paradigm for leveraging generative models in medical applications.
We propose a diffusion-based data engine, termed MRGen, which enables generation conditioned on text prompts and masks.
arXiv Detail & Related papers (2024-12-04T16:34:22Z) - 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) - End-to-end autoencoding architecture for the simultaneous generation of
medical images and corresponding segmentation masks [3.1133049660590615]
We present an end-to-end architecture based on the Hamiltonian Variational Autoencoder (HVAE)
This approach yields an improved posterior distribution approximation compared to traditional Variational Autoencoders (VAE)
Our method outperforms generative adversarial conditions, showcasing enhancements in image quality synthesis.
arXiv Detail & Related papers (2023-11-17T11:56:53Z) - A 3D generative model of pathological multi-modal MR images and
segmentations [3.4806591877889375]
We propose brainSPADE3D, a 3D generative model for brain MRI and associated segmentations.
The proposed joint imaging-segmentation generative model is shown to generate high-fidelity synthetic images and associated segmentations.
We demonstrate how the model can alleviate issues with segmentation model performance when unexpected pathologies are present in the data.
arXiv Detail & Related papers (2023-11-08T09:36:37Z) - ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic
Diffusion Models [69.9178140563928]
Colonoscopy analysis is essential for assisting clinical diagnosis and treatment.
The scarcity of annotated data limits the effectiveness and generalization of existing methods.
We propose an Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy images that benefit the downstream tasks.
arXiv Detail & Related papers (2023-09-03T07:55:46Z) - 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) - Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data.
The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale.
Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
arXiv Detail & Related papers (2022-01-07T07:46:01Z) - Enhancing MR Image Segmentation with Realistic Adversarial Data
Augmentation [17.539828821476224]
We propose an adversarial data augmentation approach to improve the efficiency in utilizing training data.
We present a generic task-driven learning framework, which jointly optimize a data augmentation model and a segmentation network during training.
The proposed adversarial data augmentation does not rely on generative networks and can be used as a plug-in module in general segmentation networks.
arXiv Detail & Related papers (2021-08-07T11:32:37Z)
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.