Due to the three-dimensional nature of CT- or MR-scans, generative modeling of medical images is a particularly challenging task. Existing approaches mostly apply patch-wise, slice-wise, or cascaded generation techniques to fit the high-dimensional data into the limited GPU memory. However, these approaches may introduce artifacts and potentially restrict the model's applicability for certain downstream tasks. This work presents WDM, a wavelet-based medical image synthesis framework that applies a diffusion model on wavelet decomposed images. The presented approach is a simple yet effective way of scaling 3D diffusion models to high resolutions and can be trained on a single \SI{40}{\giga\byte} GPU. Experimental results on BraTS and LIDC-IDRI unconditional image generation at a resolution of $128 \times 128 \times 128$ demonstrate state-of-the-art image fidelity (FID) and sample diversity (MS-SSIM) scores compared to recent GANs, Diffusion Models, and Latent Diffusion Models. Our proposed method is the only one capable of generating high-quality images at a resolution of $256 \times 256 \times 256$, outperforming all comparing methods.
MotionWavelet: Human Motion Prediction via Wavelet Manifold Learning
Imperial College London（帝国理工学院）；The University of Hong Kong（香港大学）；University of Pennsylvania（宾夕法尼亚大学）；Hong Kong University of Science and Technology（香港科技大学）；Nanyang Technological University（南洋理工大学）；Shanghai AI Lab（上海人工智能实验室）；University of Cambridge（剑桥大学）；Texas A&M University（德州农工大学）
Brain Tumour Removing and Missing Modality Generation using 3D WDM
Center Algoritmi / LASI, University of Minho（米尼奥大学Algoritmi中心 / LASI）；Computer Algorithms for Medicine Laboratory（计算机医学算法实验室）；Institute for AI in Medicine (IKIM), University Medicine Essen（埃森大学医学医院医学人工智能研究所）；Institute of Medical Informatics, University Hospital RWTH Aachen（亚琛工业大学医学院医学信息学研究所）；Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen（亚琛工业大学医学院口腔颌面外科）；Institute of Computer Graphics and Vision, Graz University of Technology（格拉茨工业大学计算机图形学与视觉研究所）；Cancer Research Center Cologne Essen (CCCE), University Medicine Essen（埃森大学医学医院科隆埃森癌症研究中心）；German Cancer Consortium (DKTK), Partner Site Essen（德国癌症联盟埃森合作站点）；Department of Physics, TU Dortmund University（多特蒙德工业大学物理系）
In this paper, we consider the problem of super-resolution recons-truction. This is a hot topic because super-resolution reconstruction has a wide range of applications in the medical field, remote sensing monitoring, and criminal investigation. Compared with traditional algorithms, the current super-resolution reconstruction algorithm based on deep learning greatly improves the clarity of reconstructed pictures. Existing work like Super-Resolution Using a Generative Adversarial Network (SRGAN) can effectively restore the texture details of the image. However, experimentally verified that the …
RVS College of Engineering and Technology（RVS工程技术学院）
In recent years, many research achievements are made in the medical image fusion field. Fusion is basically extraction of best of inputs and conveying it to the output. Medical Image fusion means that several of various modality image information is comprehended together to form one image to express its information. The aim of image fusion is to integrate complementary and redundant information. In this paper, a multimodal image fusion algorithm based on the dual-tree complex wavelet transform (DT-CWT) and adaptive particle swarm optimization (APSO) is proposed. Fusion is achieved through the …
A huge advantage of the wavelet transform in image and video compression is its scalability. Wavelet-based coding of medical computed tomography (CT) data becomes more and more popular. While much effort has been spent on encoding of the wavelet coefficients, the extension of the transform by a compensation method as in video coding has not gained much attention so far. We will analyze two compensation methods for medical CT data and compare the characteristics of the displacement compensated wavelet transform with video data. We will show that for thorax CT data the transform coding gain can …
In this research paper, authors propose multimodal brain image registration using discrete wavelet transform(DWT) followed by Gaussian pyramids. The reference and target images are decomposed into their LL, LH, HL and LL DWT coefficients and then are processed for image registration using Gaussian pyramids. The image registration is also done using Gaussian pyramids only and wavelets transforms only for comparison. The quality of registration is measured by comparing the maximum MI values used by the three methods and also by comparing their correlation coefficients. Our proposed technique pro…
Medical applications like Computed Tomography (CT) or Magnetic Resonance Tomography (MRT) often require an efficient scalable representation of their huge output volumes in the further processing chain of medical routine. A downscaled version of such a signal can be obtained by using image and video coders based on wavelet transforms. The visual quality of the resulting lowpass band, which shall be used as a representative, can be improved by applying motion compensation methods during the transform. This paper presents a new approach of using the distorted edge lengths of a mesh-based compens…
Chinese Academy of Sciences（中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Walton Institute for Information and Communication Systems Science（沃尔顿信息与通信系统科学研究所）；Waterford Institute of Technology（沃特福德理工学院）；University of Johannesburg（约翰内斯堡大学）；University of Sindh（信德大学）；Sungkyunkwan University（成均馆大学）
Deep learning based single image super resolution (SISR) algorithms has revolutionized the overall diagnosis framework by continually improving the architectural components and training strategies associated with convolutional neural networks (CNN) on low-resolution images. However, existing work lacks in two ways: i) the SR output produced exhibits poor texture details, and often produce blurred edges, ii) most of the models have been developed for a single modality, hence, require modification to adapt to a new one. This work addresses (i) by proposing generative adversarial network (GAN) wi…
fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting
Department of Biomedical Engineering, University of Basel, Switzerland Department of Quantitative Biomedicine, University of Zurich, Switzerland Helmholtz AI, Helmholtz Zentrum München, Germany Department of Diagnostic；Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Germany TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Germany
In recent years, many research achievements are made in the medical image fusion field. Medical Image fusion means that several of various modality image information is comprehended together to form one image to express its information. The aim of image fusion is to integrate complementary and redundant information. CT/MRI is one of the most common medical image fusion. These medical modalities give information about different diseases. Complementary information is offered by CT and MRI. CT provides the best information about denser tissue and MRI offers better information on soft tissue. Ther…
Yale Center for Medical Informatics（耶鲁医学信息学中心）；VA CT Healthcare System（美国退伍军人事务部康涅狄格医疗系统）
Medical image datasets can have large number of images representing patients with different health conditions and various disease severity. When dealing with raw unlabeled image datasets, the large number of samples often makes it hard for experts and non-experts to understand the variety of images present in a dataset. Supervised learning methods rely on labeled images which requires a considerable effort by medical experts to first understand the communities of images present in the data and then labeling the images. Here, we propose an algorithm to facilitate the automatic identification of…
For scalable coding, a high quality of the lowpass band of a wavelet transform is crucial when it is used as a downscaled version of the original signal. However, blur and motion can lead to disturbing artifacts. By incorporating feasible compensation methods directly into the wavelet transform, the quality of the lowpass band can be improved. The displacement in dynamic medical 3-D+t volumes from Computed Tomography is mainly given by expansion and compression of tissue over time and can be modeled well by mesh-based methods. We extend a 2-D mesh-based compensation method to three dimensions …
An efficient scalable data representation is an important task especially in the medical area, e.g. for volumes from Computed Tomography (CT) or Magnetic Resonance Tomography (MRT), when a downscaled version of the original signal is needed. Image and video coders based on wavelet transforms provide an adequate way to naturally achieve scalability. This paper presents a new approach for improving the visual quality of the lowpass band by using a novel graph-based method for motion compensation, which is an important step considering data compression. We compare different kinds of neighborhoods…
Multi-modal Medical Neurological Image Fusion using Wavelet Pooled Edge Preserving Autoencoder
Visvesvaraya National Institute of Technology Nagpur（维斯瓦瓦拉亚那格浦尔国家理工学院）；Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；All India Institute of Medical Sciences Nagpur（全印那格浦尔医学科学研究所）
For the lossless compression of dynamic 3-D+t volumes as produced by medical devices like Computed Tomography, various coding schemes can be applied. This paper shows that 3-D subband coding outperforms lossless HEVC coding and additionally provides a scalable representation, which is often required in telemedicine applications. However, the resulting lowpass subband, which shall be used as a downscaled representative of the whole original sequence, contains a lot of ghosting artifacts. This can be alleviated by incorporating motion compensation methods into the subband coder. This results in …
Professional applications like telemedicine often require scalable lossless coding of sensitive data. 3-D subband coding has turned out to offer good compression results for dynamic CT data and additionally provides a scalable representation in terms of low- and highpass subbands. To improve the visual quality of the lowpass subband, motion compensation can be incorporated into the lifting structure, but leads to inferior compression results at the same time. Prior work has shown that a denoising filter in the update step can improve the compression ratio. In this paper, we present a new proce…
We propose a novel deep learning framework for fast prediction of boundaries of two-dimensional simply connected domains using wavelets and Multi Resolution Analysis (MRA). The boundaries are modelled as (piecewise) smooth closed curves using wavelets and the so-called Pyramid Algorithm. Our network architecture is a hybrid analog of the U-Net, where the down-sampling path is a two-dimensional encoder with learnable filters, and the upsampling path is a one-dimensional decoder, which builds curves up from low to high resolution levels. Any wavelet basis induced by a MRA can be used. This flexi…
Lossless compression of dynamic 2D+t and 3D+t medical data is challenging regarding the huge amount of data, the characteristics of the inherent noise, and the high bit depth. Beyond that, a scalable representation is often required in telemedicine applications. Motion Compensated Temporal Filtering works well for lossless compression of medical volume data and additionally provides temporal, spatial, and quality scalability features. To achieve a high quality lowpass subband, which shall be used as a downscaled representative of the original data, graph-based motion compensation was recently …
Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland（巴塞尔大学生物医学工程系）；Olten Cantonal Hospital, Olten, Switzerland（奥尔滕县医院）；Department of Orthopedics, University Children's Hospital Basel, Basel, Switzerland（巴塞尔大学儿童医院骨科部）
Efficient lossless coding of medical volume data with temporal axis can be achieved by motion compensated wavelet lifting. As side benefit, a scalable bit stream is generated, which allows for displaying the data at different resolution layers, highly demanded for telemedicine applications. Additionally, the similarity of the temporal base layer to the input sequence is preserved by the use of motion compensated temporal filtering. However, for medical sequences the overall rate is increased due to the specific noise characteristics of the data. The use of denoising filters inside the lifting …
Lossless image coding is a crucial task especially in the medical area, e.g., for volumes from Computed Tomography or Magnetic Resonance Tomography. Besides lossless coding, compensated wavelet lifting offers a scalable representation of such huge volumes. While compensation methods increase the details in the lowpass band, they also vary the characteristics of the wavelet coefficients, so an adaption of the coefficient coder should be considered. We propose a simple invertible extension for JPEG 2000 that can reduce the filesize for lossless coding of the highpass band by 0.8% on average with…
Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mamba
Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）；College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区生物医学工程学院）；Human Phenome Institute, Fudan University（复旦大学人类表型组研究院）；Inselspital, University of Bern（伯尔尼大学因塞尔医院）；Technical University of Munich（慕尼黑工业大学）
Med-URWKV†: Toward Enhanced Pretrained Pure VRWKV Models for Medical Image Segmentation
College of Computer Science, Nankai University（南开大学计算机科学学院）；Key Laboratory of Data and Intelligent System Security, Ministry of Education（教育部数据与智能系统安全重点实验室）；School of Medicine, Nankai University（南开大学医学院）；Nankai University Eye institute, Nankai University（南开大学眼科研究院）；Tianjn Eye Hospital（天津眼科医院）；Haihe Lab of ITAI（海河ITAI实验室）
École de Technologie Supérieure, Université du Québec（魁北克大学高等技术学院）
Breast cancer is a health problem that affects mainly the female population. An early detection increases the chances of effective treatment, improving the prognosis of the disease. In this regard, computational tools have been proposed to assist the specialist in interpreting the breast digital image exam, providing features for detecting and diagnosing tumors and cancerous cells. Nonetheless, detecting tumors with a high sensitivity rate and reducing the false positives rate is still challenging. Texture descriptors have been quite popular in medical image analysis, particularly in histopath…
Chest radiographs are used for the diagnosis of multiple critical illnesses (e.g., Pneumonia, heart failure, lung cancer), for this reason, systems for the automatic or semi-automatic analysis of these data are of particular interest. An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists, ultimately allowing for better medical care of lung-, heart- and chest-related conditions. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information that is typically lost in the down-sampling …
Due to their high resolution, dynamic medical 2D+t and 3D+t volumes from computed tomography (CT) and magnetic resonance tomography (MR) reach a size which makes them very unhandy for teleradiologic applications. A lossless scalable representation offers the advantage of a down-scaled version which can be used for orientation or previewing, while the remaining information for reconstructing the full resolution is transmitted on demand. The wavelet transform offers the desired scalability. A very high quality of the lowpass sub-band is crucial in order to use it as a down-scaled representation.…
LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion
The University of Sydney（悉尼大学）；The Chinese University of Hong Kong (CUHK)（香港中文大学）；Northeastern University（东北大学）；University College London（伦敦大学学院）；Shun Hing Institute of Advanced Engineering, CUHK（香港中文大学信兴高等工程研究所）
Parameter choices in HaarPSI for IQA with medical images
1 Medical University of Vienna, Center for Medical Physics；Biomedical Engineering, Vienna, Austria 2 University of Cambridge, Department of Physics, Cambridge, UK 3 University of Cambridge, Department of Radiology, Cambridge, UK 4 Cambridge University Hospitals, Department of Radiology, Cambridge, UK 5 University of Cambridge, Department of Applied Mathematics；Theoretical Physics, Cambridge, UK 6 University of Cambridge, Heart \& Lung Research Institute, Cambridge, UK 7 King's College London, School of Biomedical Engineering \& Imaging Sciences, London, UK Corresponding author. E-mail
HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；NEC Laboratories America Inc.（NEC美国实验室）；Norwegian University of Science and Technology（挪威科技大学）；ETH Zürich（苏黎世联邦理工学院）；Peng Cheng Laboratory（鹏城实验室）
Annually 8500 neonatal deaths are reported in the US due to respiratory failure. Recently, Lung Ultrasound (LUS), due to its radiation free nature, portability, and being cheaper is gaining wide acceptability as a diagnostic tool for lung conditions. However, lack of highly trained medical professionals has limited its use especially in remote areas. To address this, an automated screening system that captures characteristics of the LUS patterns can be of significant assistance to clinicians who are not experts in lung ultrasound (LUS) images. In this paper, we propose a feature extraction met…
X-ray "ghost" imaging has drawn great attention for its potential to lower radiation dose in medical diagnosis. For practical implementation, however, the efficiency and image quality have to be greatly improved. Here we demonstrate a computational ghost imaging scheme where a bucket detector and specially designed modulation masks are used, together with a new robust deep learning algorithm in which a compressed set of Hadamard matrices is incorporated into a multi-level wavelet convolutional neural network. By this means we have obtained an image of a real object from only 18.75% of the Nyqu…
