Cell Image Analysis Techniques

Latest papers

• From Retrieval to Reasoning: Agentic Mechanism Prediction from Cell Painting Profiles

  Jiayuan Chen, Botao Yu, Tianyu Liu, Thai-Hoang Pham, Meng Wu, Ping Zhang · 30 September 2026

  Cell Painting is a high-content morphological profiling assay widely used for phenotype-based biological inference, with mechanism of action (MOA) prediction as a central application. Existing approaches largely formulate Cell Painting-based inference as representation matching, assigning prediction…

• BIABench: Evaluating AI agents on real-world bioimage analysis tasks

  Zixuan Pan, Davide Panzeri, Lukas Johanns, Marilin Moor, Yu Zhou, Hedi Peterson, Yiyu Shi, Jianxu Chen · 29 September 2026

  Artificial-intelligence (AI) agents hold promise for automating bioimage analysis, yet no benchmark evaluates whether they can carry out real-world analyses end to end. Such analyses are hard for agents because 2D images, 3D volumes and time-lapse sequences are often too large to read as context, so…

• Improving Molecular-Morphology Contrastive Pretraining using Deep-Learning-based Morphology Profiles

  Jie Li, Kathryn E. Kirchoff, Dante A. Pertusi, Zhizhuo Zhang · 28 September 2026

  Recent advancements in image-based profiling techniques have enabled the collection of high-volume cell morphology data, allowing new molecular embedding models to learn from the experimental phenotypic perturbations of a molecule in a cell. Previously, we developed Molecule-Morphology Contrastive P…

• AxonSynth: Domain-Randomized Synthetic Data for Zero-Shot 3D Axon Segmentation in Light-Sheet Microscopy

  Edward Gaibor, Kyriaki-Margarita Bintsi, Carmen Luz Leiva Ureta, Zayneb Bellatif, Chiara Maffei, Wenze Li, Elizabeth Hillman, Ya\"el Balbastre, Anastasia Yendiki · 28 September 2026

  Accurate segmentation of axons in 3D microscopy data is important for analyzing white-matter organization, but dense ground truth labels are expensive to obtain. Existing supervised axon segmentation methods rely on target-domain annotations and can be brittle when tissue type, species, modality, or…

• Samples, Sources, Space: Decomposing Data Scale in Spatially Structured Representation Learning of Human Brain Microarchitecture

  Christian Schiffer, Mathis Bode, Thomas Lippert, Katrin Amunts, Timo Dickscheid · 28 September 2026

  Scaling studies typically represent training data by a single count of samples. For hierarchically and spatially structured data, however, the same number of samples can be drawn from few or many sources and distributed differently across the underlying domain. We therefore study data scaling as an …

• SomaNet: Weakly Supervised Learning for Instance Soma Segmentation in 3D Electron Microscopy with Partial Annotations

  Mohammad Khateri, Morteza Ghahremani, Jussi Tohka, Alejandra Sierra · 22 September 2026

  Soma instance segmentation, i.e., identifying and delineating individual cell somas as distinct instances, is crucial for cellular analysis and connectomic reconstruction. Three-dimensional electron microscopy (3D EM) provides nanometer-scale resolution for capturing fine-grained soma morphology. Ho…

• Evaluating Transformation Models for pCLE Mosaic Registration

  Ahmed Aboelela, Johannes Barcsay, Jana Friedhof, Miguel Gon\c{c}alves, Alexander Hann, Katharina Breininger · 22 September 2026

  Confocal Laser Endomicroscopy (CLE) provides real-time, cellular-resolution optical biopsy but has a narrow field of view, which image mosaicing can extend to provide anatomical context. Because of line-by-line acquisition, probe motion, and probe-tissue interaction, frame alignment generally requir…

• Catena: A Comprehensive Software Suite for Large-Scale Connectomics

  Samia Mohinta, Pedro G\'omez-G\'alvez, Shi Yan Lee, Daniel Franco-Barranco, Michael Clayton, Stephan Preibisch, Jan Funke, Albert Cardona · 21 September 2026

  The gold standard datasets for mapping connectomes are electron microscopy volumes of densely labeled neural tissue at nanometer resolution. Yet reconstructing and proofreading neuronal arbors and annotating all synapses requires pipelining multiple software tools that are often fragmented, inconsis…

• Extending Decoupled Attention to Dense Prediction and Masked Training for Multi-Channel Images

  Umar Marikkar, Sameed Husain, Muhammad Awais, Sara Atito · 21 September 2026

  Multi-Channel imaging (MCI) data differs fundamentally from natural images, as each channel records a semantically distinct signal rather than a colour band. To adapt vision encoders to MCI data, Multi-Channel Vision Transformers (MC-ViTs) tokenize each channel independently and concatenate the resu…

• End-to-End Cell Detection via Instance-aware Graph Modeling

  Ruochen Liu, Yalin Zheng, Jingxin Liu, Jianfeng Zhang, Shoujun Huang, Dexing Kong, Haofeng Li, Wei Lou · 16 September 2026

  Accurate cell detection and classification are crucial for pathological analysis, directly affecting diagnostic accuracy and treatment planning. To capture complex cellular interactions beyond visual appearance within the tumor microenvironment, several approaches have employed graph neural networks…

• Same Encoder, Different Winner: A Paired-View Framework for Cell Painting Encoder Evaluation

  Tim Treis, Nikita Moshkov, Johan Fredin Haslum, Shantanu Singh, Fabian J. Theis · 14 September 2026

  Vision encoders for Cell Painting are typically ranked by a single evaluation, commonly replicate mean average precision (mAP). We introduce CP-BG-Bench, a paired-view evaluation framework that holds the central cell fixed across four matched views (raw crop C, segmented S, and density-augmented var…

• QuPAINT: Physics-Aware Multimodal Reasoning for Quantum Material Characterization

  Sankalp Pandey, Xuan-Bac Nguyen, Hoang-Quan Nguyen, Tim Faltermeier, Nicholas Borys, Hugh Churchill, Khoa Luu · 14 September 2026

  Characterizing two-dimensional (2D) quantum materials by optical microscopy requires localizing exfoliated flakes and determining their layer thickness from subtle optical contrast and interference color to select suitable flakes for device fabrication. However, models face synthetic-to-real domain …

• Multimodal Taxonomic Conditioning for Generative Plankton Imagery

  Daniela Ivanova, Ozgu Goksu, Nicolas Pugeault · 11 September 2026

  Automated plankton imaging produces severely long-tailed datasets, where the rare taxa of greatest ecological interest have too few images to train or evaluate classifiers reliably. We generate synthetic plankton imagery conditioned on taxonomy: a CLIP encoder is adapted on a large plankton corpus w…

• Pretraining and Distillation Matter More Than Architecture Family for Label-Free Single-Cell Classification

  Philip Graemer, Giuseppe Di Caprio · 10 September 2026

  Choosing a deep learning architecture for label-free single-cell classification remains an open question, with microscopy benchmarks reporting conflicting conclusions about CNNs versus transformers. We present a controlled benchmark on LIVECell phase-contrast microscopy data using source-image-disjo…

• SkNeXt enables topology-guided neuronal reconstruction from petabyte-scale microscopy data

  Jiayi Ding, Hu Zhao · 10 September 2026

  Recent advances in high-resolution fluorescence and electron microscopy have enabled nanoscale imaging across increasingly large brain volumes, but the resulting terabyte- to petabyte-scale datasets make complete neuronal reconstruction prohibitively expensive in computation, data movement, and manu…

• Exemplar: Classical Priors Complement Frozen Features for Few-Shot Microscopy Segmentation at Native Resolution

  Michal Pr\r{u}\v{s}ek, Adam Novoz\'amsk\'y, Filip \v{S}roubek · 4 September 2026

  Segmenting a new biomedical dataset usually means a domain-specific model trained on substantial annotation, or a foundation model steered at inference time. We present Exemplar, a few-shot segmenter that fuses a frozen DINOv3 backbone with a fixed bank of classical native-resolution filter response…

• MorphoCLIP: Text-Supervised Contrastive Learning for Perturbation Matching in Cell Painting Images

  Sukhrobbek Ilyosbekov (Northeastern University), Shubham Gajjar (Northeastern University), Rongfei Jin (Northeastern University) · 25 August 2026

  Cell Painting microscopy captures how cells change after a chemical or genetic perturbation. Connecting these images to the perturbations that produced them could make large imaging screens easier to search and interpret, but the task remains difficult because biological effects are subtle and techn…

• Lymphocyte Mimicry Correction via Region-Level Tissue Reasoning and Unbalanced Optimal Transport

  Xiang Li, Yuqi Wang, Casey C. Heirman, Jihye Heo, Kyle J. Lafata · 19 August 2026

  Cell mimicry arises when different cell types appear morphologically similar. Human pathologists resolve this ambiguity using surrounding tissue context, whereas current vision models either lack contextual reasoning (cell foundation models) or cannot operate at the cell level (pathology MLLMs). We …

• Unsupervised Learning of Cell Instances with Generative Routing Pyramids

  Ziwen Liu, Martin Weigert · 18 August 2026

  Identifying and representing object instances such as cells or nuclei is a common task in microscopy image analysis. Established machine learning workflows typically use supervised detection or segmentation followed by feature extraction or classification, which requires manual annotations and treat…

• Joint Flow Matching Enables Continuous Dose-Conditioned Cell Morphing

  Lea Bogensperger, Manuela Merlo, Martin Baumgartner, Michael Krauthammer, Bernard Ciraulo · 18 August 2026

  Generative modeling has shown increasing promise for predicting cellular perturbation effects under chemical compound treatments. Existing approaches either model perturbation as a distribution-to-distribution mapping without explicit concentration handling, or treat concentration as a discrete clas…

• Conditional Neural Optimal Transport for Predicting Cellular Phenotypes from Molecular Structure

  Gauthier Avit\'e, Maxime Sanchez-Renauld, Nicolas Bourriez, Auguste Genovesio · 17 August 2026

  High-content microscopy enables systematic profiling of cellular responses to chemical perturbations, but the scale of the chemical space makes exhaustive phenotypic characterization experimentally infeasible. This motivates computational models that can predict image-derived phenotypes without acqu…

• Chartography: A Benchmark for Professional Chart Understanding

  Suhaas Garre, Chris Mutty, Sushant Mehta, Edwin Chen · 12 August 2026

  Professionals across medicine, engineering, finance, manufacturing, and the sciences often make consequential decisions from charts. Existing chart benchmarks do not sufficiently measure this ability: they are dominated by bar, line, and pie formats, rely on shorter reasoning chains, and are nearing…

• Automatic Field-of-View Adjustment for a View-Expansive Microscope via LSTM-Based Gaze and Pipette Motion Interpretation

  Kenta Yokoe, Takuya Hara, Tadayoshi Aoyama · 12 August 2026

  Intracytoplasmic sperm injection (ICSI) operators frequently adjust the field-of-view (FOV) during procedures, which interrupts workflow and increases procedure time. Conventional microscopes require manual objective lens switching and illumination adjustments to achieve different FOV sizes. We prop…

• Beyond Isotropic Assumptions: Continuity-Constrained Segmentation and GPU Morphometry for Nanoscale GBM Analysis

  Arash Fatehi, Robin Ebbestad, Linus Butt, Hans Blom, Sigrid Lundberg, Hannes Olauson, Hjalmar Brismar, David Unnersj\"o-Jess, Thomas Benzing, Katarzyna Bozek · 11 August 2026

  Confocal microscopy of optically cleared and swelled tissue resolves complex biological structures in 3D, but such acquisitions are highly anisotropic: along the under-sampled axial direction the structure can appear discontinuous, hampering reconstruction and automated quantitative analysis. The us…

• NeuroRefiner: Morphology-Aware Multi-Agent Refinement for 3D Fluorescence Microscopy Neuron Segmentation

  Haiyang Yan, Jinyue Guo, Yanchao Zhang, Bingqing Wang, Zhenchen Li, Jing Liu, Jiazheng Liu, Linlin Li, Hua Han · 11 August 2026

  Accurate 3D neuron segmentation in fluorescence microscopy is critical for neuroscience. However, the sparse and elongated morphology of neurons poses significant challenges to existing segmentation methods. These methods struggle to preserve both local details and global topology, leading to fragme…