Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval
Imaging technology center, Fujifilm corporation（富士胶片公司成像技术中心）；Center for Artificial Intelligence Research, University of Tsukuba（筑波大学人工智能研究中心）；Department of computer science and engineering, Waseda university（早稻田大学计算机科学与工程系）
Akira Kudo, Yoshiro Kitamura, Yuanzhong Li, Satoshi Iizuka, Edgar Simo-Serra
备注：
10 pages, 6 figures, Accepted to Machine Learning for Medical Image Reconstruction (MLMIR) at MICCAI 2019
Many CT slice images are stored with large slice intervals to reduce storage size in clinical practice. This leads to low resolution perpendicular to the slice images (i.e., z-axis), which is insufficient for 3D visualization or image analysis. In this paper, we present a novel architecture based on conditional Generative Adversarial Networks (cGANs) with the goal of generating high resolution images of main body parts including head, chest, abdomen and legs. However, GANs are known to have a difficulty with generating a diversity of patterns due to a phenomena known as mode collapse. To overcome the lack of generated pattern variety, we propose to condition the discriminator on the different body parts. Furthermore, our generator networks are extended to be three dimensional fully convolutional neural networks, allowing for the generation of high resolution images from arbitrary fields of view. In our verification tests, we show that the proposed method obtains the best scores by PSNR/SSIM metrics and Visual Turing Test, allowing for accurate reproduction of the principle anatomy in high resolution. We expect that the proposed method contribute to effective utilization of the existing vast amounts of thick CT images stored in hospitals.
MLP-SRGAN: A Single-Dimension Super Resolution GAN using MLP-Mixer
Toronto Metropolitan University（多伦多都会大学）；University of Toronto（多伦多大学）；Institute of Biomedical Engineering, Science and Technology (iBEST)（生物医学工程科学与技术研究所）
Research Center for Medical Big Data, National Institute of Informatics（日本情报学研究所医疗大数据研究中心）；LPixel Inc.（LPixel公司）；Graduate School of Information Science and Technology, The University of Tokyo（东京大学信息科学与技术研究生院）；Department of Radiology, University of Cambridge（剑桥大学放射科）；Cancer Research UK Cambridge Centre（英国癌症研究中心剑桥中心）；Department of Artificial Intelligence, ELTE Eötvös Loránd University（厄特沃什罗兰大学人工智能系）；Department of Rehabilitation Medicine, Juntendo University School of Medicine（顺天堂大学医学部康复医学科）；International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo（东京大学国际神经智能研究中心（WPI-IRCN））
Unsupervised learning can discover various unseen diseases, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with disease stages.…
LPIXEL Inc.（LPIXEL公司）；University of Cambridge（剑桥大学）；National Institute of Informatics（国立信息学研究所）；National Center for Global Health and Medicine（国立国际医疗研究中心）；Juntendo University（顺天堂大学）；ELTE Eötvös Loránd University（厄特沃什·罗兰大学）；The University of Tokyo（东京大学）
Unsupervised learning can discover various unseen abnormalities, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with eith…
Automatic text recognition from ancient handwritten record images is an important problem in the genealogy domain. However, critical challenges such as varying noise conditions, vanishing texts, and variations in handwriting make the recognition task difficult. We tackle this problem by developing a handwritten-to-machine-print conditional Generative Adversarial network (HW2MP-GAN) model that formulates handwritten recognition as a text-Image-to-text-Image translation problem where a given image, typically in an illegible form, is converted into another image, close to its machine-print form. …
The University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；The Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
Histopathological analysis is crucial in artery characterization for coronary artery disease (CAD). However, histology requires an invasive and time-consuming process. In this paper, we propose to generate virtual histology staining using Optical Coherence Tomography (OCT) images to enable real-time histological visualization. We develop a deep learning network, namely Coronary-GAN, to transfer coronary OCT images to virtual histology images. With a special consideration on the structural constraints in coronary OCT images, our method achieves better image generation performance than the conve…
Comparative Analysis of GAN and Diffusion for MRI-to-CT translation
Department of Computer Science, University of Copenhagen, Copenhagen, Denmark（哥本哈根大学计算机科学系）；Department of Oncology, Rigshospitalet, Copenhagen, Denmark（哥本哈根医院肿瘤科）
Computer-Aided-Diagnosis (CADx) systems assist radiologists with identifying and classifying potentially malignant pulmonary nodules on chest CT scans using morphology and texture-based (radiomic) features. However, radiomic features are sensitive to differences in acquisitions due to variations in dose levels and slice thickness. This study investigates the feasibility of generating a normalized scan from heterogeneous CT scans as input. We obtained projection data from 40 low-dose chest CT scans, simulating acquisitions at 10%, 25% and 50% dose and reconstructing the scans at 1.0mm and 2.0mm…
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；University of Miami（迈阿密大学）；National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）；Networking Health（网络健康）
We present a novel algorithm that is able to classify COVID-19 pneumonia from CT Scan slices using a very small sample of training images exhibiting COVID-19 pneumonia in tandem with a larger number of normal images. This algorithm is able to achieve high classification accuracy using as few as 10 positive training slices (from 10 positive cases), which to the best of our knowledge is one order of magnitude fewer than the next closest published work at the time of writing. Deep learning with extremely small positive training volumes is a very difficult problem and has been an important topic d…
Deep generative models are known to be able to model arbitrary probability distributions. Among these, a recent deep generative model, dubbed sliceGAN, proposed a new way of using the generative adversarial network (GAN) to capture the micro-structural characteristics of a two-dimensional (2D) slice and generate three-dimensional (3D) volumes with similar properties. While 3D micrographs are largely beneficial in simulating diverse material behavior, they are often much harder to obtain than their 2D counterparts. Hence, sliceGAN opens up many interesting directions of research by learning the…
To super-resolve the through-plane direction of a multi-slice 2D magnetic resonance (MR) image, its slice selection profile can be used as the degeneration model from high resolution (HR) to low resolution (LR) to create paired data when training a supervised algorithm. Existing super-resolution algorithms make assumptions about the slice selection profile since it is not readily known for a given image. In this work, we estimate a slice selection profile given a specific image by learning to match its internal patch distributions. Specifically, we assume that after applying the correct slice …
Eindhoven University of Technology（埃因霍温理工大学）；Philips Research Laboratories（飞利浦研究实验室）；Philips Healthcare（飞利浦医疗）
There has been considerable interest in the MR physics-based simulation of a database of virtual cardiac MR images for the development of deep-learning analysis networks. However, the employment of such a database is limited or shows suboptimal performance due to the realism gap, missing textures, and the simplified appearance of simulated images. In this work we 1) provide image simulation on virtual XCAT subjects with varying anatomies, and 2) propose sim2real translation network to improve image realism. Our usability experiments suggest that sim2real data exhibits a good potential to augme…
Deep learning requires large datasets for training (convolutional) networks with millions of parameters. In neuroimaging, there are few open datasets with more than 100 subjects, which makes it difficult to, for example, train a classifier to discriminate controls from diseased persons. Generative adversarial networks (GANs) can be used to synthesize data, but virtually all research is focused on 2D images. In medical imaging, and especially in neuroimaging, most datasets are 3D or 4D. Here we therefore present preliminary results showing that a 3D progressive growing GAN can be used to synthe…
Zhejiang Sci-Tech University（浙江理工大学）；Zhejiang University（浙江大学）；Chongqing University（重庆大学）；Beijing Institute of Technology（北京理工大学）；Royal Brompton Hospital（皇家布朗普顿医院）；Imperial College London（伦敦帝国理工学院）
High-resolution magnetic resonance images can provide fine-grained anatomical information, but acquiring such data requires a long scanning time. In this paper, a framework called the Fused Attentive Generative Adversarial Networks(FA-GAN) is proposed to generate the super-resolution MR image from low-resolution magnetic resonance images, which can reduce the scanning time effectively but with high resolution MR images. In the framework of the FA-GAN, the local fusion feature block, consisting of different three-pass networks by using different convolution kernels, is proposed to extract image…
South China University of Technology（华南理工大学）；University of Utah（犹他大学）
We introduce the idea of inter-slice image augmentation whereby the numbers of the medical images and the corresponding segmentation labels are increased between two consecutive images in order to boost medical image segmentation accuracy. Unlike conventional data augmentation methods in medical imaging, which only increase the number of training samples directly by adding new virtual samples using simple parameterized transformations such as rotation, flipping, scaling, etc., we aim to augment data based on the relationship between two consecutive images, which increases not only the number b…
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Maryland School of Dentistry（马里兰大学牙科学院）
Tagged magnetic resonance imaging (MRI) is a widely used imaging technique for measuring tissue deformation in moving organs. Due to tagged MRI's intrinsic low anatomical resolution, another matching set of cine MRI with higher resolution is sometimes acquired in the same scanning session to facilitate tissue segmentation, thus adding extra time and cost. To mitigate this, in this work, we propose a novel dual-cycle constrained bijective VAE-GAN approach to carry out tagged-to-cine MR image synthesis. Our method is based on a variational autoencoder backbone with cycle reconstruction constrain…
STS-GAN: Can We Synthesize Solid Texture with High Fidelity from Arbitrary 2D Exemplar?
Shandong Provincial Key Laboratory of Preparation and Measurement of Building Materials（山东省建筑材料制备与测试技术重点实验室）；South China University of Technology（华南理工大学）；Shandong Provincial Key Laboratory of Network Based Intelligent Computing（山东省网络环境智能计算技术重点实验室）；Quan Cheng Laboratory（泉城实验室）；Shandong Qiuqi Analysis Instrument Co., Ltd.（山东求奇分析仪器有限公司）；University of Waterloo（滑铁卢大学）；University of Jinan（济南大学）
Arizona State University（亚利桑那州立大学）；Mila – Quebec Artificial Intelligence Institute（魁北克人工智能研究所（Mila））；Mayo Clinic（梅奥诊所）
Generative adversarial networks (GANs) have ushered in a revolution in image-to-image translation. The development and proliferation of GANs raises an interesting question: can we train a GAN to remove an object, if present, from an image while otherwise preserving the image? Specifically, can a GAN "virtually heal" anyone by turning his medical image, with an unknown health status (diseased or healthy), into a healthy one, so that diseased regions could be revealed by subtracting those two images? Such a task requires a GAN to identify a minimal subset of target pixels for domain translation,…
Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Inserm U1216（法国国家健康与医学研究院 U1216）；Grenoble Institut for Neurosciences（格勒诺布尔神经科学研究所）
ImUnity is an original deep-learning model designed for efficient and flexible MR image harmonization. A VAE-GAN network, coupled with a confusion module and an optional biological preservation module, uses multiple 2D-slices taken from different anatomical locations in each subject of the training database, as well as image contrast transformations for its self-supervised training. It eventually generates 'corrected' MR images that can be used for various multi-center population studies. Using 3 open source databases (ABIDE, OASIS and SRPBS), which contain MR images from multiple acquisition …
Using real road testing to optimize autonomous driving algorithms is time-consuming and capital-intensive. To solve this problem, we propose a GAN-based model that is capable of generating high-quality images across different domains. We further leverage Contrastive Learning to train the model in a self-supervised way using image data acquired in the real world using real sensors and simulated images from 3D games. In this paper, we also apply an Attention Mechanism module to emphasize features that contain more information about the source domain according to their measurement of significance…
Eindhoven University of Technology（埃因霍温理工大学）；Philips Research Laboratories（飞利浦研究实验室）；Philips Healthcare（飞利浦医疗）
Generative adversarial networks (GANs) have provided promising data enrichment solutions by synthesizing high-fidelity images. However, generating large sets of labeled images with new anatomical variations remains unexplored. We propose a novel method for synthesizing cardiac magnetic resonance (CMR) images on a population of virtual subjects with a large anatomical variation, introduced using the 4D eXtended Cardiac and Torso (XCAT) computerized human phantom. We investigate two conditional image synthesis approaches grounded on a semantically-consistent mask-guided image generation techniqu…
Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin
Department of Computing Science, Umeå University（计算科学系，乌梅大学）；Department of Diagnostics and Intervention, Biomedical Engineering and Radiation Physics, Umeå University（诊断与介入系，生物医学工程与放射物理，乌梅大学）
Self-Supervised Z-Slice Augmentation for 3D Bio-Imaging via Knowledge Distillation
Collège de France（法兰西学院）；Université PSL（巴黎文理研究大学）；Université Grenoble-Alpes（格勒诺布尔-阿尔卑斯大学）；Sorbonne University（索邦大学）；National Institutes of Health（美国国立卫生研究院）；Hospices Civils de Lyon（里昂公立医院）；Claude Bernard Lyon 1 University（里昂第一大学克洛德·贝尔纳）
