While Positron emission tomography (PET) imaging has been widely used in diagnosis of number of diseases, it has costly acquisition process which involves radiation exposure to patients. However, magnetic resonance imaging (MRI) is a safer imaging modality that does not involve patient's exposure to radiation. Therefore, a need exists for an efficient and automated PET image generation from MRI data. In this paper, we propose a new frequency-aware attention U-net for generating synthetic PET images. Specifically, we incorporate attention mechanism into different U-net layers responsible for estimating low/high frequency scales of the image. Our frequency-aware attention Unet computes the attention scores for feature maps in low/high frequency layers and use it to help the model focus more on the most important regions, leading to more realistic output images. Experimental results on 30 subjects from Alzheimers Disease Neuroimaging Initiative (ADNI) dataset demonstrate good performance of the proposed model in PET image synthesis that achieved superior performance, both qualitative and quantitative, over current state-of-the-arts.
Cross-conditioned Diffusion Model for Medical Image to Image Translation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Xi’an Jiaotong University（西安交通大学）；The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong University of Science and Technology（香港科技大学）
CAD-Unet: A Capsule Network-Enhanced Unet Architecture for Accurate Segmentation of COVID-19 Lung Infections from CT Images
School of Information Engineering, Ningxia University（宁夏大学信息工程学院）；Ningxia Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West, Ningxia University（宁夏人工智能与信息安全重点实验室）；School of Mathematics and Computer Science, Ningxia Normal University（宁夏师范学院数学与计算机科学学院）；School of Advanced Interdisciplinary Studies, Ningxia University（宁夏大学先进交叉学科学院）
A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers
Centre des Matériaux (MAT)（材料中心）；CNRS（法国国家科学研究中心）；Centre de Mise en Forme des Matériaux (CEMEF)（材料成型中心）；Centre de Morphologie Mathématique (CMM)（数学形态学中心）；Mines Paris（巴黎高等矿业学院）；Université PSL（巴黎文理研究大学）
TSUBF-Net: Trans-Spatial UNet-like Network with Bi-direction Fusion for Segmentation of Adenoid Hypertrophy in CT
Shenzhen University General Hospital（深圳大学总医院）；College of Mechatronics and Engineering, Shenzhen University（深圳大学机电与工程学院）；College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）；Research Center of Medical Plasma Technology, Shenzhen University（深圳大学医用等离子体技术研究中心）；Shenzhen Lihuy Medical Tech. co. Ltd.（深圳利护医疗科技有限公司）
Towards Segmenting the Invisible: An End-to-End Registration and Segmentation Framework for Weakly Supervised Tumour Analysis
Institute of Technical；Business Information Systems, Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany Human Technopole, Milan, Italy Institute of Medical Engineering, Faculty of Electrical Engineering；Information Technology, Otto von Guericke University Magdeburg, Magdeburg, Germany Centre for Behavioural Brain Sciences, Magdeburg, Germany
SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images
Philips Research North America（飞利浦北美研究院）；Massachusetts General Hospital/Harvard Medical School（麻省总医院/哈佛医学院）；The Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital/Harvard Medical School（麻省总医院/哈佛医学院高级医学计算与分析中心）
Tumor-aware augmentation with task-guided attention analysis improves rectal cancer segmentation from magnetic resonance images
Department of Medical Physics, Memorial Sloan Kettering Cancer Center（医学物理系，纪念斯隆凯特琳癌症中心）；Department of Radiology, Memorial Sloan Kettering Cancer Center（放射学系，纪念斯隆凯特琳癌症中心）；Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center（放射肿瘤学系，纪念斯隆凯特琳癌症中心）；Department of Surgery, Memorial Sloan Kettering Cancer Center（外科系，纪念斯隆凯特琳癌症中心）；Department of Medicine, Memorial Sloan Kettering Cancer Center（医学系，纪念斯隆凯特琳癌症中心）
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）
Sorbonne Université（索邦大学）；Paris Brain Institute - ICM（巴黎脑研究所-ICM）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共援助医院）；Hôpital de la Pitié Salpêtrière（皮蒂耶-萨尔佩特里埃医院）
Quantifying the distribution and morphology of tau protein structures in brain tissues is key to diagnosing Alzheimer's Disease (AD) and its subtypes. Recently, deep learning (DL) models such as UNet have been successfully used for automatic segmentation of histopathological whole slide images (WSI) of biological tissues. In this study, we propose a DL-based methodology for semantic segmentation of tau lesions (i.e., neuritic plaques) in WSI of postmortem patients with AD. The state of the art in semantic segmentation of neuritic plaques in human WSI is very limited. Our study proposes a basel…
