Synthetic Augmentation for Anatomical Landmark Localization using DDPMs
- URL: http://arxiv.org/abs/2410.12489v2
- Date: Thu, 17 Oct 2024 08:03:34 GMT
- Title: Synthetic Augmentation for Anatomical Landmark Localization using DDPMs
- Authors: Arnela Hadzic, Lea Bogensperger, Simon Johannes Joham, Martin Urschler,
- Abstract summary: diffusion-based generative models have recently started to gain attention for their ability to generate high-quality synthetic images.
We propose a novel way to assess the quality of the generated images using a Markov Random Field (MRF) model for landmark matching and a Statistical Shape Model (SSM) to check landmark plausibility.
- Score: 0.22499166814992436
- License:
- Abstract: Deep learning techniques for anatomical landmark localization (ALL) have shown great success, but their reliance on large annotated datasets remains a problem due to the tedious and costly nature of medical data acquisition and annotation. While traditional data augmentation, variational autoencoders (VAEs), and generative adversarial networks (GANs) have already been used to synthetically expand medical datasets, diffusion-based generative models have recently started to gain attention for their ability to generate high-quality synthetic images. In this study, we explore the use of denoising diffusion probabilistic models (DDPMs) for generating medical images and their corresponding heatmaps of landmarks to enhance the training of a supervised deep learning model for ALL. Our novel approach involves a DDPM with a 2-channel input, incorporating both the original medical image and its heatmap of annotated landmarks. We also propose a novel way to assess the quality of the generated images using a Markov Random Field (MRF) model for landmark matching and a Statistical Shape Model (SSM) to check landmark plausibility, before we evaluate the DDPM-augmented dataset in the context of an ALL task involving hand X-Rays.
Related papers
- Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data [4.069144210024564]
We propose an augmented DDGAN architecture, Ambient DDGAN (ADDGAN) for learning realistic SOMs from noisy image data.
The ability of the proposed ADDGAN to learn realistic SOMs from noisy image data is demonstrated.
arXiv Detail & Related papers (2025-01-31T12:40:43Z) - 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) - 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) - Diffuse-UDA: Addressing Unsupervised Domain Adaptation in Medical Image Segmentation with Appearance and Structure Aligned Diffusion Models [31.006056670998852]
The scarcity and complexity of voxel-level annotations in 3D medical imaging present significant challenges.
This disparity affects the fairness of artificial intelligence algorithms in healthcare.
We introduce Diffuse-UDA, a novel method leveraging diffusion models to tackle Unsupervised Domain Adaptation (UDA) in medical image segmentation.
arXiv Detail & Related papers (2024-08-12T08:21:04Z) - 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) - High-Fidelity Image Synthesis from Pulmonary Nodule Lesion Maps using
Semantic Diffusion Model [10.412300404240751]
Lung cancer has been one of the leading causes of cancer-related deaths worldwide for years.
Deep learning, computer-assisted diagnosis (CAD) models based on learning algorithms can accelerate the screening process.
However, developing robust and accurate models often requires large-scale and diverse medical datasets with high-quality annotations.
arXiv Detail & Related papers (2023-05-02T01:04:22Z) - Simulating Realistic MRI variations to Improve Deep Learning model and
visual explanations using GradCAM [0.0]
We use a modified HighRes3DNet model for solving brain MRI volumetric landmark detection problem.
Grad-CAM produces a coarse localization map highlighting the regions the model is focusing.
arXiv Detail & Related papers (2021-11-01T11:14:23Z) - Generative Residual Attention Network for Disease Detection [51.60842580044539]
We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning.
We generate a corresponding radiology image in a target domain while preserving the identity of the patient.
We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
arXiv Detail & Related papers (2021-10-25T14:15:57Z) - Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for
Thoracic Disease Identification [83.6017225363714]
deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance.
For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming.
In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
arXiv Detail & Related papers (2021-02-26T02:29:30Z) - G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for
Biomarker Identification and Disease Classification [49.53651166356737]
We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers.
We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
arXiv Detail & Related papers (2021-01-27T19:28:04Z) - Realistic Adversarial Data Augmentation for MR Image Segmentation [17.951034264146138]
We propose an adversarial data augmentation method for training neural networks for medical image segmentation.
Our model generates plausible and realistic signal corruptions, which models the intensity inhomogeneities caused by a common type of artefacts in MR imaging: bias field.
We show that such an approach can improve the ability generalization and robustness of models as well as provide significant improvements in low-data scenarios.
arXiv Detail & Related papers (2020-06-23T20:43:18Z)
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.