Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
University Hospital Aachen（亚琛大学医院）；RWTH Aachen University（亚琛工业大学）；Ocumeda AG（奥库梅达股份公司）；University Hospital Heidelberg（海德堡大学医院）；University Hospital Würzburg（维尔茨堡大学医院）；University of Mainz（美因茨大学）；RWTH Aachen（亚琛工业大学）
Firas Khader, Gustav Mueller-Franzes, Soroosh Tayebi Arasteh, Tianyu Han, Christoph Haarburger, Maximilian Schulze-Hagen, Philipp Schad, Sandy Engelhardt, Betti… 展开作者
Firas Khader, Gustav Mueller-Franzes, Soroosh Tayebi Arasteh, Tianyu Han, Christoph Haarburger, Maximilian Schulze-Hagen, Philipp Schad, Sandy Engelhardt, Bettina Baessler, Sebastian Foersch, Johannes Stegmaier, Christiane Kuhl, Sven Nebelung, Jakob Nikolas Kather, Daniel Truhn
Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models in particular have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen and Stable Diffusion. However, their use in medicine, where image data typically comprises three-dimensional volumes, has not been systematically evaluated. Synthetic images may play a crucial role in privacy preserving artificial intelligence and can also be used to augment small datasets. Here we show that diffusion probabilistic models can synthesize high quality medical imaging data, which we show for Magnetic Resonance Images (MRI) and Computed Tomography (CT) images. We provide quantitative measurements of their performance through a reader study with two medical experts who rated the quality of the synthesized images in three categories: Realistic image appearance, anatomical correctness and consistency between slices. Furthermore, 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 (dice score 0.91 vs. 0.95 without vs. with synthetic data). The code is publicly available on GitHub: https://github.com/FirasGit/medicaldiffusion.
We cast the problem of image denoising as a domain translation problem between high and low noise domains. By modifying the cycleGAN model, we are able to learn a mapping between these domains on unpaired retinal optical coherence tomography images. In quantitative measurements and a qualitative evaluation by ophthalmologists, we show how this approach outperforms other established methods. The results indicate that the network differentiates subtle changes in the level of noise in the image. Further investigation of the model's feature maps reveals that it has learned to distinguish retinal l…
National Tsing Hua University（国立清华大学）；Florida International University（佛罗里达国际大学）；Lehigh University（理海大学）；Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Academy of Medical Science（广东省医学科学院）；University of Notre Dame（圣母大学）
Deep learning had already demonstrated its power in medical images, including denoising, classification, segmentation, etc. All these applications are proposed to automatically analyze medical images beforehand, which brings more information to radiologists during clinical assessment for accuracy improvement. Recently, many medical denoising methods had shown their significant artifact reduction result and noise removal both quantitatively and qualitatively. However, those existing methods are developed around human-vision, i.e., they are designed to minimize the noise effect that can be perce…
Peking University（北京大学）；Academy for Advanced Interdisciplinary Studies（前沿交叉学科研究院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
We propose a novel and unified method, measurement-conditioned denoising diffusion probabilistic model (MC-DDPM), for under-sampled medical image reconstruction based on DDPM. Different from previous works, MC-DDPM is defined in measurement domain (e.g. k-space in MRI reconstruction) and conditioned on under-sampling mask. We apply this method to accelerate MRI reconstruction and the experimental results show excellent performance, outperforming full supervision baseline and the state-of-the-art score-based reconstruction method. Due to its generative nature, MC-DDPM can also quantify the unce…
Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all conventional methods, deep learning based models have shown a great promise. These methods are however limited for requirement of large training sample size and high computational costs. In this paper we show that using small sample size, denoising autoencoders constructed using convolutional layers can be used for efficient denoising of medical images. Heterogeneous images can be…
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院高级医学计算与分析中心）；Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院戈登医学成像中心）
Deep neural networks have been proved efficient for medical image denoising. Current training methods require both noisy and clean images. However, clean images cannot be acquired for many practical medical applications due to naturally noisy signal, such as dynamic imaging, spectral computed tomography, arterial spin labeling magnetic resonance imaging, etc. In this paper we proposed a training method which learned denoising neural networks from noisy training samples only. Training data in the acquisition domain was split to two subsets and the network was trained to map one noisy set to the…
University of Electronic Science and Technology of China（电子科技大学）；Sichuan University（四川大学）
Deep learning has achieved notable performance in the denoising task of low-quality medical images and the detection task of lesions, respectively. However, existing low-quality medical image denoising approaches are disconnected from the detection task of lesions. Intuitively, the quality of denoised images will influence the lesion detection accuracy that in turn can be used to affect the denoising performance. To this end, we propose a play-and-plug medical image denoising framework, namely Lesion-Inspired Denoising Network (LIDnet), to collaboratively improve both denoising performance and…
Image denoising is of great importance for medical imaging system, since it can improve image quality for disease diagnosis and downstream image analyses. In a variety of applications, dynamic imaging techniques are utilized to capture the time-varying features of the subject, where multiple images are acquired for the same subject at different time points. Although signal-to-noise ratio of each time frame is usually limited by the short acquisition time, the correlation among different time frames can be exploited to improve denoising results with shared information across time frames. With t…
Indian Institute of Information Technology Vadodara（瓦多达拉印度信息技术学院）
Medical image acquisition is often intervented by unwanted noise that corrupts the information content. This paper introduces an unsupervised medical image denoising technique that learns noise characteristics from the available images and constructs denoised images. It comprises of two blocks of data processing, viz., patch-based dictionaries that indirectly learn the noise and residual learning (RL) that directly learns the noise. The model is generalized to account for both 2D and 3D images considering different medical imaging instruments. The images are considered one-by-one from the stac…
RelativeFlow: Taming Medical Image Denoising Learning with Noisy Reference
School of Computer Science and Engineering, Southeast University, China（东南大学计算机科学与工程学院，中国）；Department of Biomedical Engineering, Case Western Reserve University, USA（凯斯西储大学生物医学工程系，美国）；School of Instrument Science and Engineering, Southeast University, China（东南大学仪器科学与工程学院，中国）；Department of Optical Sciences, University of Arizona, USA（亚利桑那大学光学科学系，美国）
Optical coherence tomography (OCT) is a prevalent non-invasive imaging method which provides high resolution volumetric visualization of retina. However, its inherent defect, the speckle noise, can seriously deteriorate the tissue visibility in OCT. Deep learning based approaches have been widely used for image restoration, but most of these require a noise-free reference image for supervision. In this study, we present a diffusion probabilistic model that is fully unsupervised to learn from noise instead of signal. A diffusion process is defined by adding a sequence of Gaussian noise to self-…
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods
Saw Swee Hock School of Public Health, National University of Singapore（新加坡国立大学苏瑞福公共卫生学院）；University of Rouen Normandy（鲁昂诺曼底大学）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）
Distributed Deep Learning for Medical Image Denoising with Data Obfuscation
Computer Engineering Department, King Fahd University of Petroleum and Minerals（国王法赫德石油与矿物大学计算机工程系）；King Fahd University of Petroleum and Minerals（国王法赫德石油与矿物大学）；SDAIA-KFUPM Joint Research Center for Artificial Intelligence（SDAIA-KFUPM人工智能联合研究中心）
Canon Medical Research Europe（佳能医疗欧洲研究中心）；University of Glasgow（格拉斯哥大学）；University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（艾伦·图灵研究所）
Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection perform…
Information Technology University（信息技术大学）；University of Toronto（多伦多大学）
Among the plethora of techniques devised to curb the prevalence of noise in medical images, deep learning based approaches have shown the most promise. However, one critical limitation of these deep learning based denoisers is the requirement of high-quality noiseless ground truth images that are difficult to obtain in many medical imaging applications such as X-rays. To circumvent this issue, we leverage recently proposed approach of [7] that incorporates Stein's Unbiased Risk Estimator (SURE) to train a deep convolutional neural network without requiring denoised ground truth X-ray data. Our…
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心图像科学研究所）；Eindhoven University of Technology（埃因霍温理工大学）
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations. We study the effect the choice of latent space distribution has on capturing the uncertainty in the reference segmentations using the LIDC-IDRI dataset. We show that the choice of distribution affects the sample diversity of the predictions and their overlap with respect to the reference segmentations. For the LIDC-IDRI dataset, we show that u…
We propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but labelled data is in limited supply. We consider the specific case of classifying skin lesions as either malignant or benign. In this setting, the proposed approach -- the semi-supervised, denoising adversarial autoencoder -- is able to utilise vast amounts of unlabelled data to learn a representation for skin lesions, and small amounts of labelled data to assign class labels based on the learned representation. We analyse the contributions…
Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Generative Models
Instituto Superior Técnico（高等技术学院）；Universidade de Lisboa（里斯本大学）；Digital Surgery LAB（数字外科实验室）；Champalimaud Foundation（尚帕利莫基金会）；Faculdade de Medicina, Universidade de Lisboa（里斯本大学医学院）；Instituto de Telecomunicações（电信研究所）
We propose a novel unpaired image-to-image translation method that uses denoising diffusion probabilistic models without requiring adversarial training. Our method, UNpaired Image Translation with Denoising Diffusion Probabilistic Models (UNIT-DDPM), trains a generative model to infer the joint distribution of images over both domains as a Markov chain by minimising a denoising score matching objective conditioned on the other domain. In particular, we update both domain translation models simultaneously, and we generate target domain images by a denoising Markov Chain Monte Carlo approach tha…
Birla Institute of Technology Science Pilani（比尔拉理工学院皮拉尼分校）
In this work, we present Eformer - Edge enhancement based transformer, a novel architecture that builds an encoder-decoder network using transformer blocks for medical image denoising. Non-overlapping window-based self-attention is used in the transformer block that reduces computational requirements. This work further incorporates learnable Sobel-Feldman operators to enhance edges in the image and propose an effective way to concatenate them in the intermediate layers of our architecture. The experimental analysis is conducted by comparing deterministic learning and residual learning for the …
Deep convolutional neural networks (DCNNs) have contributed many breakthroughs in segmentation tasks, especially in the field of medical imaging. However, \textit{domain shift} and \textit{corrupted annotations}, which are two common problems in medical imaging, dramatically degrade the performance of DCNNs in practice. In this paper, we propose a novel robust cross-denoising framework using two peer networks to address domain shift and corrupted label problems with a peer-review strategy. Specifically, each network performs as a mentor, mutually supervised to learn from reliable samples selec…
Uncertainty quantification in inverse medical imaging tasks with deep learning has received little attention. However, deep models trained on large data sets tend to hallucinate and create artifacts in the reconstructed output that are not anatomically present. We use a randomly initialized convolutional network as parameterization of the reconstructed image and perform gradient descent to match the observation, which is known as deep image prior. In this case, the reconstruction does not suffer from hallucinations as no prior training is performed. We extend this to a Bayesian approach with M…
Rician Denoising Diffusion Probabilistic Models For Sodium Breast MRI Enhancement
University of Cambridge（剑桥大学）；Universitat de Barcelona（巴塞罗那大学）；Haukeland University Hospital（豪克兰大学医院）；University of York（约克大学）；Manchester Metropolitan University（曼彻斯特城市大学）
MPI-CBG/PKS (CSBD)（马克斯·普朗克分子细胞生物学与遗传学研究所/普朗克计算生物学中心（德累斯顿系统生物学中心））；Brno University of Technology（布尔诺理工大学）
Today, Convolutional Neural Networks (CNNs) are the leading method for image denoising. They are traditionally trained on pairs of images, which are often hard to obtain for practical applications. This motivates self-supervised training methods such as Noise2Void~(N2V) that operate on single noisy images. Self-supervised methods are, unfortunately, not competitive with models trained on image pairs. Here, we present 'Probabilistic Noise2Void' (PN2V), a method to train CNNs to predict per-pixel intensity distributions. Combining these with a suitable description of the noise, we obtain a compl…
Task-based Regularization in Penalized Least-Squares for Binary Signal Detection Tasks in Medical Image Denoising
University of Michigan-Shanghai Jiao Tong University Joint Institute（密歇根大学-上海交通大学联合研究院）；Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学未来科技全球研究院）；Wyant College of Optical Sciences, University of Arizona（亚利桑那大学怀恩特光学科学学院）；Department of Medical Imaging, University of Arizona（亚利桑那大学医学影像系）
Low-dose computed tomography (LDCT) is an important topic in the field of radiology over the past decades. LDCT reduces ionizing radiation-induced patient health risks but it also results in a low signal-to-noise ratio (SNR) and a potential compromise in the diagnostic performance. In this paper, to improve the LDCT denoising performance, we introduce the conditional denoising diffusion probabilistic model (DDPM) and show encouraging results with a high computational efficiency. Specifically, given the high sampling cost of the original DDPM model, we adapt the fast ordinary differential equat…
Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院戈登医学成像中心）；Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院高级医学计算与分析中心）；University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
Due to various physical degradation factors and limited counts received, PET image quality needs further improvements. The denoising diffusion probabilistic models (DDPM) are distribution learning-based models, which try to transform a normal distribution into a specific data distribution based on iterative refinements. In this work, we proposed and evaluated different DDPM-based methods for PET image denoising. Under the DDPM framework, one way to perform PET image denoising is to provide the PET image and/or the prior image as the network input. Another way is to supply the prior image as th…
Denoising Diffusion Models for Anomaly Localization in Medical Images
School of Computation, Information and Technology, Technical University of Munich（慕尼黑技术大学计算、信息与技术学院）；Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany（生物医学影像机器学习研究所，海德堡慕尼黑德国）；Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland（巴塞尔大学生物医学工程系，瑞士Allschwil）；School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom（伦敦国王学院生物医学工程与影像科学学院，英国）；Department of Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, USA（耶鲁大学医学院生物医学信息学与数据科学系，美国新罕布什尔州新 Haven）；Yale Biomedical Imaging Institute, Yale University, New Haven, CT, USA（耶鲁大学生物医学影像研究院，美国新罕布什尔州新 Haven）
University of Texas Southwestern Medical Centre（德克萨斯大学西南医学中心）
Despite the indispensable role of X-ray computed tomography (CT) in diagnostic medicine field, the associated ionizing radiation is still a major concern considering that it may cause genetic and cancerous diseases. Decreasing the exposure can reduce the dose and hence the radiation-related risk, but will also induce higher quantum noise. Supervised deep learning can be used to train a neural network to denoise the low-dose CT (LDCT). However, its success requires massive pixel-wise paired LDCT and normal-dose CT (NDCT) images, which are rarely available in real practice. To alleviate this pro…
Diffusion models have shown promising results on single-image super-resolution and other image- to-image translation tasks. Despite this success, they have not outperformed state-of-the-art GAN models on the more challenging blind super-resolution task, where the input images are out of distribution, with unknown degradations. This paper introduces SR3+, a diffusion-based model for blind super-resolution, establishing a new state-of-the-art. To this end, we advocate self-supervised training with a combination of composite, parameterized degradations for self-supervised training, and noise-cond…
Zhejiang University-University of Illinois Urbana-Champaign Institute（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合学院）；Zhejiang University（浙江大学）
Reducing the radiation dose in computed tomography (CT) is important to mitigate radiation-induced risks. One option is to employ a well-trained model to compensate for incomplete information and map sparse-view measurements to the CT reconstruction. However, reconstruction from sparsely sampled measurements is insufficient to uniquely characterize an object in CT, and a learned prior model may be inadequate for unencountered cases. Medical modal translation from magnetic resonance imaging (MRI) to CT is an alternative but may introduce incorrect information into the synthesized CT images in a…
