MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images
Boston University（波士顿大学）；Stanford University（斯坦福大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）；University of Pittsburgh（匹兹堡大学）
Yanwu Xu, Li Sun, Wei Peng, Shuyue Jia, Katelyn Morrison, Adam Perer, Afrooz Zandifar, Shyam Visweswaran, Motahhare Eslami, Kayhan Batmanghelich
This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly used in medical imaging, current state-of-the-art approaches are limited to low-resolution outputs and underutilize radiology reports' abundant information. The radiology reports can enhance the generation process by providing additional guidance and offering fine-grained control over the synthesis of images. Nevertheless, expanding text-guided generation to high-resolution 3D images poses significant memory and anatomical detail-preserving challenges. Addressing the memory issue, we introduce a hierarchical scheme that uses a modified UNet architecture. We start by synthesizing low-resolution images conditioned on the text, serving as a foundation for subsequent generators for complete volumetric data. To ensure the anatomical plausibility of the generated samples, we provide further guidance by generating vascular, airway, and lobular segmentation masks in conjunction with the CT images. The model demonstrates the capability to use textual input and segmentation tasks to generate synthesized images. The results of comparative assessments indicate that our approach exhibits superior performance compared to the most advanced models based on GAN and diffusion techniques, especially in accurately retaining crucial anatomical features such as fissure lines, airways, and vascular structures. This innovation introduces novel possibilities. This study focuses on two main objectives: (1) the development of a method for creating images based on textual prompts and anatomical components, and (2) the capability to generate new images conditioning on anatomical elements. The advancements in image generation can be applied to enhance numerous downstream tasks.
University of Edinburgh（爱丁堡大学）；Canon Medical Research Europe（佳能医疗欧洲研究中心）；The Alan Turing Institute（艾伦·图灵研究所）
Acquiring annotated data at scale with rare diseases or conditions remains a challenge. It would be extremely useful to have a method that controllably synthesizes images that can correct such underrepresentation. Assuming a proper latent representation, the idea of a "latent vector arithmetic" could offer the means of achieving such synthesis. A proper representation must encode the fidelity of the input data, preserve invariance and equivariance, and permit arithmetic operations. Motivated by the ability to disentangle images into spatial anatomy (tensor) factors and accompanying imaging (ve…
AnF-DiffPET: Anatomy- and Frequency-Guided Diffusion for PET/CT Denoising
College of Medicine and Biological Information Engineering, Northeastern University（东北大学医学与生物信息工程学院）；Tohoku University School of Medicine（东北大学医学院）；Department of Clinical Neurosciences, University of Cambridge（剑桥大学临床神经科学系）
PHASOR: Anatomy- and Phase-Consistent Volumetric Diffusion for CT Virtual Contrast Enhancement
Shanghai Key Lab of Intelligent Information Processing, School of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院上海市智能信息处理重点实验室）；Institute of Science and Technology for Brain-inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University（复旦大学类脑智能科学与技术研究院及教育部脑科学前沿中心）；Shanghai Center for Brain Science and Brain-inspired Technology（上海脑科学与类脑研究中心）；Department of Radiology, First Affiliated Hospital of College of Medical Science, Zhejiang University（浙江大学医学院附属第一医院放射科）；Alibaba DAMO Academy（阿里巴巴达摩院）
DanceCrafter: Fine-Grained Text-Driven Controllable Dance Generation via Choreographic Syntax
East China Normal University（东华大学）；Beijing Dance Academy（北京舞蹈学院）；Beijing University of Posts and Telecommunications（北京邮电大学）；New York University Shanghai（纽约大学上海分校）；Zhongguancun Academy（中关村学院）；Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究院）
seg2med: a bridge from artificial anatomy to multimodal medical images
Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University（计算机辅助临床医学，曼海姆医学院，海德堡大学）；Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg（模式识别实验室，埃尔兰根-纽伦堡弗里德里希-亚历山大大学）；Department of Radiology and Nuclear Medicine, University Medical Center Mannheim（放射学与核医学系，曼海姆大学医学中心）；Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University（曼海姆智能医学研究所，曼海姆医学院，海德堡大学）；Optical Bioimaging Laboratory, Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore（光学生物成像实验室，生物医学工程系，设计与工程学院，新加坡国立大学）
Conditional Latent Diffusion Model with Fourier-based Motion Modelling for Virtual Population Synthesis
Centre for Computational Imaging and Modelling in Medicine (CIMIM)（计算医学成像与建模中心）；University of Manchester（曼彻斯特大学）；Christabel Pankhurst Institute（克里斯塔贝尔·潘克赫斯特研究所）；Department of Computer Science（计算机科学系）；Division of Informatics, Imaging & Data Sciences（信息学、成像与数据科学分会）；Department of Electrical & Electronic Engineering（电子与电气工程系）；NIHR Manchester Biomedical Research Centre, Manchester Academic Health Sciences Centre, University of Manchester（尼日利亚卫生研究委员会曼彻斯特生物医学研究中心、曼彻斯特学术健康科学中心、曼彻斯特大学）
Learning Global and Local Features of Normal Brain Anatomy for Unsupervised Abnormality Detection
National Cancer Center Research Institute（国家癌症中心研究所）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；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）；Research Center for Advanced Science and Technology, The University of Tokyo（东京大学先进科学技术研究中心）；National Cancer Center Hospital（国家癌症中心医院）
SLIM-Diff: Shared Latent Image-Mask Diffusion with Lp loss for Data-Scarce Epilepsy FLAIR MRI
ITIS Software, University of M\'alaga, Spain Department of Computer Languages；Computer Science, University of M\'alaga, Spain IBIMA Plataforma BIONAND, Instituto de Investigaci\'on Biom\'edica de M\'alaga, Spain Department of Radiology, Hospital Regional Universitario de M\'alaga, Spain
Efficient Image-to-Image Schrödinger Bridge for CT Field of View Extension
Institute of Medical Technology, Peking University Health Science Center（北京大学人民医院医学技术研究所）；Shanghai Cancer Center, Fudan University（复旦大学上海癌症中心）；Department of Electrical and Computer Engineering, University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校电气与计算机工程系）；Beijing Key Laboratory of Intelligent Neuromodulation and Brain Disorder Treatment（北京智能神经调控与脑疾病治疗重点实验室）
Deep Learning-Based Cross-Anatomy CT Synthesis Using Adapted nnResU-Net with Anatomical Feature Prioritized Loss
Univ. Bordeaux, CNRS, LaBRI, UMR 5800, F-33400 Talence, France（波尔多大学，法国国家科学研究中心，LaBRI研究院，UMR 5800）；Universidad Autónoma de Madrid, Madrid, 28049, Spain（马德里自治大学，西班牙）；Pazmany Peter Catholic University, Budapest, 1083, Hungary（佩斯大学，匈牙利）；RIKEN Center for Integrative Medical Sciences, Medical Data Deep Learning Team, Tokyo, Japan（日本再生医学科学中心，医学数据深度学习团队）
Anatomically Faithful Artifact Suppression in SENSE Accelerated Brain MRI
East China Normal University（华东师范大学）；Affiliated Hospital of Guizhou Medical University（贵州医科大学附属医院）；West China Hospital, Sichuan University（四川大学华西医院）；Chinese Academy of Medical Sciences（中国医学科学院）；RadioDynamic Medical（镭动医疗）；Lucile Packard Children’s Hospital（露西尔·帕卡德儿童医院）
MorphiNet: A Graph Subdivision Network for Adaptive Bi-ventricle Surface Reconstruction
School of Biomedical Engineering and Imaging Sciences, King’s College London（生物医学工程与影像科学学院，伦敦国王学院）；Brain Imaging & Neuro Epidemiology Department, Luxemboug Institute of Health（脑影像与神经流行病学部门，卢森堡健康研究所）；Department of Biostatistics and Health Informatics, King’s College London（生物统计学与健康信息学系，伦敦国王学院）
ContourDiff: Unpaired Medical Image Translation with Structural Consistency
Department of Electrical and Computer Engineering, Duke University（杜克大学电气与计算机工程系）；Department of Biostatistics & Bioinformatics, Duke University（杜克大学生物统计学与生物信息学系）；Department of Radiology, Duke University（杜克大学放射学系）；Department of Computer Science, Duke University（杜克大学计算机科学系）
