Microscopy Image Analysis

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13 papers in the last four weeks, up 117% on the four weeks before. 0.1% of all new papers.

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Label-free cell counting and viability prediction with brightfield imaging and deep learning
Oct 7, 2026Amir Reza Vazifeh, Christian Zeigler, Sornanathan Meyyappan +2Microscopy Image Analysis
Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA · Waters Corporation, Immerse Cambridge, 301 Binney Street, Suite 102, Cambridge, MA 02142, USA · Waters Corporation, 34 Maple St, Milford, MA 01757, USA +1
Patch-based Querying Identifies Structures of Interest in Electron Microscopy
Oct 5, 2026Niels Vyncke, Nicolas Nadisic, Yvan Saeys +1Image Retrieval Microscopy Image Analysis
Department of Telecommunications and Information Processing, Sint-Pietersnieuwstraat 41, Ghent, 9000, Belgium · Royal Institute for Cultural Heritage (KIK-IRPA), Jubelpark 1, Brussels, 1000, Belgium · Department of Mathematics, Computer Science and Statistics, Krijgslaan 281, Ghent, 9000, Belgium
Feature identification for parameter extraction and defect detection using machine learning
Oct 5, 2026Yan Guo, Helda Pahlavani, Artem Khachaturiants +5Semiconductor Manufacturing Industrial Inspection
Nearfield Instruments, B.V. (The Netherlands)
MorphoBranch: A Fine-Structure-Preserving Workbench for Morphometric Analysis of Branched Cellular Structures
Oct 1, 2026Song Zhiying, Ling Hanyi, Wu Junyi +1Image Segmentation Microscopy Image Analysis
School of Data Science, Zhejiang University of Finance and Economics, Hangzhou, 310018, China · College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, China · School of Information Technology and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, 310018, China
AxonSynth: Domain-Randomized Synthetic Data for Zero-Shot 3D Axon Segmentation in Light-Sheet Microscopy
Sep 25, 2026Edward Gaibor, Kyriaki-Margarita Bintsi, Chiara Mauri +7Domain Randomization Synthetic-to-Real Domain Adaptation
Department of Computer Science, University of Massachusetts Boston, Boston, MA, USA · Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA · Universidad San Sebastián, Chile +4
Leakage-Safe Machine Learning for Hydrogen Embrittlement Detection in 316L Stainless Steel: A Region-Held-Out Evaluation of Texture and Deep Features in SEM Micrographs
Sep 23, 2026Muhammad Awais, Muhammad Yaseen, Abdul Shakoor +3Materials Science Microscopy Image Analysis
Dipartimento di Fisica e Astronomia “G. Galilei”, Università di Padova, Italy · Institute of Physics, Bahauddin Zakariya University, Multan, Pakistan
RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery
Sep 22, 2026Kinjalk Parth, Sebastian Varela, Andrew D. B. LeakeyPlant Phenotyping Vision Foundation Model Adaptation
Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA · Center for Advanced Bioenergy and Bioproducts Innovation, Urbana, IL 61801, USA
MiTHras: Task-specific Hierarchical Semi-supervised Contrastive Masked Autoencoder for Mitotic Figure Analysis
Sep 21, 2026Trinh T. L. Vuong, Simon Graham, Quoc Dang Vu +5Contrastive Learning Masked Autoencoders
Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA · Histofy Ltd, Coventry, United Kingdom · Department of Pathology, University Medical Center Ho Chi Minh City, Ho Chi Minh City, Vietnam +1
Spatial Action Review: A Visual Analytics Dashboard for Auditing Language-to-Action Hand-offs in Electron Microscopy
Sep 21, 2026Samia Mohinta, Albert CardonaHuman-in-the-Loop Evaluation Multimodal QA
University of Cambridge, UK · MRC Laboratory of Molecular Biology, UK
Training Neural Networks to Approach the Optimum Bayes Estimator in Dense Multi-Emitter Localization
Sep 17, 2026Yi Sun, Mona Sharifi, Muzna YummanMicroscopy Image Analysis
Electrical Engineering Department Applied and Embodied AI Lab The City College of City University of New York New York, NY 10031, USA
Pretraining and Distillation Matter More Than Architecture Family for Label-Free Single-Cell Classification
Sep 9, 2026Philip Graemer, Giuseppe Di CaprioCross-Architecture Knowledge Distillation Knowledge Distillation
Centre for the Cellular Microenvironment, Department of Biomedical Engineering, University of Strathclyde, Glasgow, UK · School of Molecular Biosciences, University of Glasgow, Glasgow, UK
SkNeXt enables topology-guided neuronal reconstruction from petabyte-scale microscopy data
Sep 9, 2026Jiayi Ding, Hu Zhao3D Reconstruction Neuroimaging
Chinese Institute for Brain Research, Beijing, China
MorphoOrgaAgent: A Foundation-Model-Based Multi-Agent System for Autonomous Organoid Analysis
Sep 8, 2026Hanyi Zhang, Maximilian Hoermann, Lion J. Gleiter +5Image Segmentation Instance Segmentation
Helmholtz AI, Helmholtz Munich - German Research Center for Environmental Health, Neuherberg, Germany · Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany · School of Computation, Information and Technology, Technical University of Munich, Munich, Germany +4
Effects of model architecture and learning strategies on deep learning-based recognition of activated sludge microscopic images and comparison with quantitative image analysis
Sep 8, 2026Suguru Hakoshima, Tomohiro Tobino, Fumiyuki NakajimaSelf-Supervised Learning Vision Transformer
Department of Urban Engineering, Graduate School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656, Tokyo, Japan · Environmental Science Center, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-0033, Tokyo, Japan · Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, 113-8657, Tokyo, Japan
FPicker: Topology-Guided Evolution for Filament Tracing in Low-SNR Microscopy
Sep 8, 2026Tingyin Zhao, Mingtao Huang, Yuan ShenCryo-Electron Microscopy Microscopy Image Analysis
Department of Electronic Engineering, Tsinghua University, Beijing, China · Beijing National Research Center for Information Science and Technology, Beijing, China
CRISP: Corneal Confocal Microscopy Real-Time Image Stitching Pipeline
Sep 7, 2026Qincheng Qiao, Puli Zhang, Jian Zhou +1Medical Image Registration Computational Imaging
Department of Endocrinology and Metabolism, Qilu Hospital, Shandong University, Jinan, China · Department of Endocrinology and Metabolism Qilu Hospital, Shandong University Jinan 250012, China · Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China +1
Exemplar: Classical Priors Complement Frozen Features for Few-Shot Microscopy Segmentation at Native Resolution
Sep 2, 2026Michal Průšek, Adam Novozámský, Filip ŠroubekImage Segmentation Few-Shot Semantic Segmentation
The Czech Academy of Sciences, Institute of Information Theory and Automation, Czechia
Scalable Bayesian Optimization of Composite Functions for Image-Based Inverse Problems in Materials Characterization
Sep 2, 2026Dasol Yoon, Poompol Buathong, Chia-Hao Lee +3Bayesian Optimization Inverse Problems
School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA · Department of Materials Science and Engineering, Cornell University, Ithaca, NY 14853, USA · Center for Applied Mathematics, Cornell University, Ithaca, NY 14853, USA +2
CORAL: A Benchmark for Structure-aware and Brain-wide Neuron Reconstruction in Light Microscopy
Aug 31, 2026Zekang Yang, Jiamin Li, Zhenghua Li +3Neuroimaging Microscopy Image Analysis
Department of Computer Science and Technology, Institute for AI, BNRist, Tsinghua University, Beijing 100084, China · Tsinghua Laboratory of Brain and Intelligence (THBI), IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China · School of Basic Medical Sciences, Tsinghua University, Beijing, 100084, China +2
Clinical Feasibility of Low-Magnification Fluorescence Imaging for Breast Cancer Margin Detection Using Texture Analysis and Deep Learning
Aug 11, 2026Pouya Afshin, Tianling Niu, Tongtong Lu +6Computational Pathology Oncology
Department of Computer Science, Georgia State University, Atlanta, GA, USA · Joint Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA · Department of Engineering and Engineering Technology, University of Wisconsin–Oshkosh, Oshkosh, WI, USA +3
Overcoming Data Scarcity and Confidentiality in Hardware Assurance via Synthetic Generation
Aug 10, 2026Gijung Lee, Ronald Wilson, Damon L. Woodard +1Synthetic-to-Real Domain Adaptation Synthetic Data Generation
Florida Institute of National Security, University of Florida, Gainesville, Florida, USA
NeuroRefiner: Morphology-Aware Multi-Agent Refinement for 3D Fluorescence Microscopy Neuron Segmentation
Aug 10, 2026Haiyang Yan, Jinyue Guo, Yanchao Zhang +6Topology-Preserving Image Segmentation Multi-Agent Systems
State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences · School of Future Technology, University of Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences +1
On-Device Multi-Species Malaria Detection with Uncertainty-Calibrated Slide-Level Aggregation
Aug 9, 2026Idaya Seidu, Ahmed Tahiru Issah, Charles B. Delahunt +1Medical Diagnosis Object Detection
Carnegie Mellon University Africa, Kigali Innovation City, Kigali, Rwanda · University of Washington, Seattle, Washington, USA
JUMP-lite: Compact, reproducible benchmarking of cell representations
Aug 7, 2026Alán F. Muñoz, Johan Fredin Haslum, Runxi Shen +2Representation Learning Microscopy Image Analysis
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
NeuroAdaptTrainer: A Fiji/ImageJ Plugin for YOLO-Based Neuron Segmentation, InteractiveCorrection and Transfer Learning
Aug 5, 2026Daniela Eraso-Casas, Gerard Villarroya-Pique, Esther Serrano-Pertierra +4Image Segmentation Instance Segmentation
aComputer Sciences Department, University of Oviedo, Asturias, Spain · bElectrical Engineering Department, University of Oviedo, Asturias, Spain · cBiomedical Engineering Center (BME), University of Oviedo, Asturias, Spain +3
Beyond Isotropic Assumptions: Continuity-Constrained Segmentation and GPU Morphometry for Nanoscale GBM Analysis
Aug 4, 2026Arash Fatehi, Robin Ebbestad, Linus Butt +7Image Segmentation 3D Medical Image Segmentation
Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany · Center for Molecular Medicine Cologne (CMMC), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany · Science for Life Laboratory, Department of Women’s and Children’s Health, Karolinska Institutet, Solna, Sweden +6
A Unified Resolution-Conditioned Framework for Orthogonal Line-Scanning Image Fusion
Aug 4, 2026Yiming Gong, Kai WangLinear Attention Microscopy Image Analysis
Department of Physics, University of Michigan, Ann Arbor, 48109, MI, USA · School of Physics and Astronomy, Sun Yat-sen University and Guangdong Provincial Key Laboratory of Quantum Metrology and Sensing, Zhuhai Campus, Zhuhai, 519082, Guangdong Province, China · Zhuhai Key Laboratory of Optoelectronic Functional Materials and Membrane Technology, Zhuhai, 519082, Guangdong Province, China
Artificial Intelligence for the Characterization of Particles and Fibers by Optical Microscopy
Aug 1, 2026Simiao Sun, Kenneth Ng, Lynn Lee +6Cross-Modal Knowledge Distillation Visual Representation Learning
Department of Chemistry, University of Hong Kong, Hong Kong SAR · Museum Studies Program, University of Hong Kong, Hong Kong SAR · Department of History, City University, Hong Kong, Hong Kong SAR +3
Physics-Aligned Self-Supervised Learning for Scientific Imaging
Jul 30, 2026Bashir Kazimi, Stefan SandfeldData Augmentation Scientific ML
Institute for Materials Data Science and Informatics (IAS-9), Forschungszentrum Jülich GmbH, 52425, Jülich, Germany · Chair of Materials Data Science and Materials Informatics, Faculty 5 – Georesources and Materials Engineering, RWTH Aachen University, 52056, Aachen, Germany
MUL-T: Decoding Spatial Cellular Architecture in Multiplexed Tissue Images
Jul 30, 2026Farzaneh Seyedshahi, Kai Rakovic, Adalberto Claudio Quiros +2Transformer Generative Modeling
Cancer Research UK Scotland Institute, Glasgow, UK · University of Glasgow, Glasgow, UK

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Label-free cell counting and viability prediction with brightfield imaging and deep learning

Amir Reza Vazifeh, Christian Zeigler, Sornanathan Meyyappan, Richard Jeske+1

Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA · Waters Corporation, Immerse Cambridge, 301 Binney Street, Suite 102, Cambridge, MA 02142, USA · Waters Corporation, 34 Maple St, Milford, MA 01757, USA+1

Microscopy Image Analysis

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Cell viability assessment is a core requirement in cell culture systems, with critical applications in biopharmaceutical manufacturing and drug development. Conventionally, it is measured by adding membrane-impermeable dyes to a sample (a process called staining), which allows compromised cell membranes to be distinguished from intact ones. However, staining has several limitations: (a) chemical agents can perturb normal cellular processes of the cells being measured, (b) it is often ambiguous to assign viability to individual cells whose membrane integrity is only partially compromised. (c) photobleaching can undermine measurement accuracy over time when using fluorescent stains, and (d) staining cannot be performed in situ or in real time. Here, we show that (1) stained cells captured under brightfield imaging contain sufficient information to distinguish live and dead cells, and (2) cells captured under unstained brightfield imaging exhibit similar image features to their stained counterparts, enabling models trained on stained cells to generalize to unstained ones. We then report the development and validation of ViabiLens, an AI-assisted software for label-free cell viability analysis. The ViabiLens combines a cell detection model for localizing individual cells with a convolutional neural network (CNN) classifier for live/dead prediction, paired with an interactive UMAP-based viewer for visualizing and exploring individual cells across the sample. Evaluated on Chinese Hamster Ovary (CHO) cells spanning a wide range of viability conditions, ViabiLens achieves a mean absolute error of 2.68% on unstained samples against fluorescence-based reference measurements. We also release a benchmark dataset for label-free cell viability analysis to facilitate future research, available at https://amirrezavazifeh.github.io/ViabiLens-Project-Page/.

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