Fluorescence microscopy allows for a detailed inspection of cells, cellular networks, and anatomical landmarks by staining with a variety of carefully-selected markers visualized as color channels. Quantitative characterization of structures in acquired images often relies on automatic image analysis methods. Despite the success of deep learning methods in other vision applications, their potential for fluorescence image analysis remains underexploited. One reason lies in the considerable workload required to train accurate models, which are normally specific for a given combination of markers, and therefore applicable to a very restricted number of experimental settings. We herein propose Marker Sampling and Excite, a neural network approach with a modality sampling strategy and a novel attention module that together enable (i) flexible training with heterogeneous datasets with combinations of markers and (ii) successful utility of learned models on arbitrary subsets of markers prospectively. We show that our single neural network solution performs comparably to an upper bound scenario where an ensemble of many networks is naïvely trained for each possible marker combination separately. In addition, we demonstrate the feasibility of this framework in high-throughput biological analysis by revising a recent quantitative characterization of bone marrow vasculature in 3D confocal microscopy datasets and further confirm the validity of our approach on an additional, significantly different dataset of microvessels in fetal liver tissues. Not only can our work substantially ameliorate the use of deep learning in fluorescence microscopy analysis, but it can also be utilized in other fields with incomplete data acquisitions and missing modalities.
Generating Fundus Fluorescence Angiography Images from Structure Fundus Images Using Generative Adversarial Networks
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences（中国科学院苏州生物医学工程技术研究所）；Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）；the Third People’s Hospital of Changzhou（常州市第三人民医院）
Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles
Technical University of Berlin（柏林工业大学）；Charité University Hospital（夏里特大学医院）；University of Heidelberg（海德堡大学）；Osaka University（大阪大学）；Singapore University of Technology and Design（新加坡科技设计大学）；German Cancer Consortium (DKTK)（德国癌症研究联盟（DKTK））；Korea University（高丽大学）；Max-Planck-Institute for Informatics（马克斯-普朗克信息研究所）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Department of Software Engineering and Theoretical Computer Science（软件工程与理论计算机科学系）；Department of Internal Medicine, Infectious Diseases and Pulmology（内科、传染病与肺病学系）；Institute of Pathology（病理学研究所）；Laboratory of Cellular Dynamics（细胞动力学实验室）；ISTD Pillar（ISTD支柱）；Department of Brain and Cognitive Engineering（脑与认知工程系）；Department of Pediatric Hematology and Oncology（儿科血液学与肿瘤学系）；Machine-Learning Group（机器学习小组）；Systems Pathology Lab（系统病理学实验室）；Laboratory of Cellular Dynamics, Immunology Frontier Research Center（细胞动力学实验室、免疫学前沿研究中心）
X-RAFT: Cross-Modal Non-Rigid Registration of Blue and White Light Neurosurgical Hyperspectral Images
Department of Surgical \& Interventional Engineering, School of Biomedical Engineering \& Imaging Sciences, King's College London, London SE1 7EH Department of Neurosurgery, King's College London Hospital NHS Foundation Trust, London SE5 9RS, UK. Department of Imaging Chemistry \& Biology, School of Biomedical Engineering \& Imaging Sciences, King's College London, London SE1 7EH, UK.
A Multimodal 3D Foundation Model for Light Sheet Fluorescence Microscopy Enables Few-Shot Segmentation, Classification, and Deblurring
Tri-Institutional Program in Computational Biology \& Medicine, Weill Cornell Medicine, New York, NY, USA Department of Radiology, Weill Cornell Medicine, New York, NY, USA Helen；Robert Appel Alzheimers Disease Research Institute, Feil Family Brain；Mind Research Institute, Weill Cornell Medicine, New York, NY, USA Graduate Program in Physiology, Biophysics；Systems Biology, Weill Cornell Medicine, New York, NY, USA Cornell Tech, New York, NY, USA Institute for Intelligent Biotechnologies (iBIO), Helmholtz Center Munich, Neuherberg, Germany Institute for Stroke；Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, Munich, Germany
Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy
National Institute of Nuclear Physics（国家核物理研究所）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；University of Bologna（博洛尼亚大学）
Deep Learning Enables Large-Scale Shape and Appearance Modeling in Total-Body DXA Imaging
University of Hawaiʻi Cancer Center（夏威夷大学癌症中心）；University of Hawaiʻi at Mānoa（夏威夷大学马诺阿分校）；Pennington Biomedical Research Center（彭宁顿生物医学研究中心）；Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP（美国国立卫生研究院国家老龄化研究所院内研究项目流行病学与人口科学实验室）；Hologic, Inc.（豪洛捷公司）
SIMPLER: H&E-Informed Representation Learning for Structured Illumination Microscopy
Kahlert School of Computing, University of Utah, Salt Lake City, UT 84112, USA Scientific Computing \& Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA Instapath Inc., Houston, TX 77021 Tulane University, Department of Biomedical Engineering, New Orleans, Louisiana, United States
