{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/cell-segmentation/papers/2","list_of":"/task/cell-segmentation","task":"Cell Segmentation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,178],"of":178,"counts":{"archive_papers_tagged":178,"with_a_code_link":91,"where_syntology_ran_a_sample":7,"not_listed_spam_title":0,"listed":178,"listed_where_code_ran":7,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":6,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":6,"listed_every_run_a_failure_of_syntologys_instrument":1,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/cell-segmentation","prev":"/task/cell-segmentation","next":null,"papers":[{"url":null,"slug":"open-source-infrastructure-for-automatic-cell","title":"Open Source Infrastructure for Automatic Cell Segmentation","date":"2024-09-12","arxiv_id":"2409.08163","repositories_listed":0,"syntology":null},{"url":null,"slug":"lensless-fiber-endomicroscopic-phase-imaging","title":"Diffusion-driven lensless fiber endomicroscopic quantitative phase imaging towards digital pathology","date":"2024-07-26","arxiv_id":"2407.18456","repositories_listed":0,"syntology":null},{"url":null,"slug":"artificial-immunofluorescence-in-a-flash","title":"Artificial Immunofluorescence in a Flash: Rapid Synthetic Imaging from Brightfield Through Residual Diffusion","date":"2024-07-25","arxiv_id":"2407.17882","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-cell-instance-segmentation-in","title":"Enhancing Cell Instance Segmentation in Scanning Electron Microscopy Images via a Deep Contour Closing Operator","date":"2024-07-22","arxiv_id":"2407.15817","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-asymmetric-mixture-model-for","title":"Deep asymmetric mixture model for unsupervised cell segmentation","date":"2024-06-03","arxiv_id":"2406.01815","repositories_listed":0,"syntology":null},{"url":null,"slug":"prior-guided-diffusion-model-for-cell","title":"Prior-guided Diffusion Model for Cell Segmentation in Quantitative Phase Imaging","date":"2024-05-10","arxiv_id":"2405.06175","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-enabled-low-cost-cell-image","title":"Active Learning Enabled Low-cost Cell Image Segmentation Using Bounding Box Annotation","date":"2024-05-02","arxiv_id":"2405.01701","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-the-cell-image-segmentation","title":"Practical Guidelines for Cell Segmentation Models Under Optical Aberrations in Microscopy","date":"2024-04-12","arxiv_id":"2404.08549","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-segmentation-and-classification","title":"Deep Learning Segmentation and Classification of Red Blood Cells Using a Large Multi-Scanner Dataset","date":"2024-03-27","arxiv_id":"2403.18468","repositories_listed":0,"syntology":null},{"url":null,"slug":"staindiffuser-multitask-dual-diffusion-model","title":"StainDiffuser: MultiTask Dual Diffusion Model for Virtual Staining","date":"2024-03-17","arxiv_id":"2403.11340","repositories_listed":0,"syntology":null},{"url":null,"slug":"pushing-the-limits-of-cell-segmentation","title":"Pushing the limits of cell segmentation models for imaging mass cytometry","date":"2024-02-06","arxiv_id":"2402.04446","repositories_listed":0,"syntology":null},{"url":null,"slug":"fdnet-frequency-domain-denoising-network-for","title":"FDNet: Frequency Domain Denoising Network For Cell Segmentation in Astrocytes Derived From Induced Pluripotent Stem Cells","date":"2024-02-05","arxiv_id":"2402.02724","repositories_listed":0,"syntology":null},{"url":null,"slug":"morphological-profiling-for-drug-discovery-in","title":"Morphological Profiling for Drug Discovery in the Era of Deep Learning","date":"2023-12-13","arxiv_id":"2312.07899","repositories_listed":0,"syntology":null},{"url":null,"slug":"cellmixer-annotation-free-semantic-cell","title":"CellMixer: Annotation-free Semantic Cell Segmentation of Heterogeneous Cell Populations","date":"2023-12-01","arxiv_id":"2312.00671","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-diffusion-probabilistic-models-for-7","title":"Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain","date":"2023-11-28","arxiv_id":"2311.16821","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-foundation-model-for-cell-segmentation","title":"A Foundation Model for Cell Segmentation","date":"2023-11-18","arxiv_id":"2311.11004","repositories_listed":0,"syntology":null},{"url":null,"slug":"defining-the-boundaries-challenges-and","title":"Defining the boundaries: challenges and advances in identifying cells in microscopy images","date":"2023-11-14","arxiv_id":"2311.08269","repositories_listed":0,"syntology":null},{"url":null,"slug":"distnet2d-leveraging-long-range-temporal","title":"DistNet2D: Leveraging long-range temporal information for efficient segmentation and tracking","date":"2023-10-30","arxiv_id":"2310.19641","repositories_listed":0,"syntology":null},{"url":null,"slug":"cupre-cross-domain-unsupervised-pre-training","title":"CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation","date":"2023-10-06","arxiv_id":"2310.03981","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-de-sparsification-matters-inducing","title":"Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning","date":"2023-09-12","arxiv_id":"2309.06439","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-instance-segmentation-with-a","title":"Semi-supervised Instance Segmentation with a Learned Shape Prior","date":"2023-09-09","arxiv_id":"2309.04888","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-multi-modality-cell-segmentation","title":"The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions","date":"2023-08-10","arxiv_id":"2308.05864","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-image-compression-on-in-vitro-cell","title":"Impact of Image Compression on In Vitro Cell Migration Analysis","date":"2023-05-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"advanced-multi-microscopic-views-cell-semi","title":"Advanced Multi-Microscopic Views Cell Semi-supervised Segmentation","date":"2023-03-21","arxiv_id":"2303.11661","repositories_listed":0,"syntology":null},{"url":null,"slug":"spacetx-a-roadmap-for-benchmarking-spatial","title":"SpaceTx: A Roadmap for Benchmarking Spatial Transcriptomics Exploration of the Brain","date":"2023-01-20","arxiv_id":"2301.08436","repositories_listed":0,"syntology":null},{"url":null,"slug":"double-u-net-for-super-resolution-and","title":"Double U-Net for Super-Resolution and Segmentation of Live Cell Images","date":"2022-12-05","arxiv_id":"2212.02028","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-melanocytic-cell-masks-from-adjacent","title":"Learning Melanocytic Cell Masks from Adjacent Stained Tissue","date":"2022-11-01","arxiv_id":"2211.00646","repositories_listed":0,"syntology":null},{"url":null,"slug":"scale-equivariant-u-net","title":"Scale Equivariant U-Net","date":"2022-10-10","arxiv_id":"2210.04508","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-characterization-of-catalytically","title":"Automated Characterization of Catalytically Active Inclusion Body Production in Biotechnological Screening Systems","date":"2022-09-30","arxiv_id":"2209.15584","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-stain-transfer-to-study-the","title":"Adversarial Stain Transfer to Study the Effect of Color Variation on Cell Instance Segmentation","date":"2022-09-01","arxiv_id":"2209.00585","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-based-self-supervision-for-semi","title":"Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation","date":"2022-08-03","arxiv_id":"2208.02105","repositories_listed":0,"syntology":null},{"url":"/paper/point2mask-a-weakly-supervised-approach-for","slug":"point2mask-a-weakly-supervised-approach-for","title":"Point2Mask: A Weakly Supervised Approach for Cell Segmentation Using Point Annotation","date":"2022-07-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-multi-object-segmentation-framework","title":"A hybrid multi-object segmentation framework with model-based B-splines for microbial single cell analysis","date":"2022-05-03","arxiv_id":"2205.01367","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-distillation-augmented-masked","title":"Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification","date":"2022-03-31","arxiv_id":"2203.16983","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-microscopy-for-content-and","title":"Differentiable Microscopy for Content and Task Aware Compressive Fluorescence Imaging","date":"2022-03-28","arxiv_id":"2203.14945","repositories_listed":0,"syntology":null},{"url":null,"slug":"cell-segmentation-from-telecentric-bright","title":"Cell segmentation from telecentric bright-field transmitted light microscopy images using a Residual Attention U-Net: a case study on HeLa line","date":"2022-03-23","arxiv_id":"2203.12290","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-cell-segmentation-using-deep","title":"Improved cell segmentation using deep learning in label-free optical microscopy images","date":"2021-09-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-for-the","title":"Unsupervised Domain Adaptation for the Histopathological Cell Segmentation through Self-Ensembling","date":"2021-07-20","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/deepcens-an-end-to-end-pipeline-for-cell-and","slug":"deepcens-an-end-to-end-pipeline-for-cell-and","title":"DeepCeNS: An end-to-end Pipeline for Cell and Nucleus Segmentation in Microscopic Images","date":"2021-07-18","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-branch-hybrid-transformer-networkfor","title":"A Multi-Branch Hybrid Transformer Networkfor Corneal Endothelial Cell Segmentation","date":"2021-05-21","arxiv_id":"2106.07557","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-blood-cell-segmentation-for-the","title":"An Efficient Blood-Cell Segmentation for the Detection of Hematological Disorders","date":"2021-03-18","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deepcervix-a-deep-learning-based-framework","title":"DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques","date":"2021-02-24","arxiv_id":"2102.12191","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-cell-segmentation-in-digital","title":"Accurate Cell Segmentation in Digital Pathology Images via Attention Enforced Networks","date":"2020-12-14","arxiv_id":"2012.07237","repositories_listed":0,"syntology":null},{"url":null,"slug":"spherical-harmonics-for-shape-constrained-3d","title":"Spherical Harmonics for Shape-Constrained 3D Cell Segmentation","date":"2020-10-23","arxiv_id":"2010.12369","repositories_listed":0,"syntology":null},{"url":null,"slug":"cellcyclegan-spatiotemporal-microscopy-image","title":"CellCycleGAN: Spatiotemporal Microscopy Image Synthesis of Cell Populations using Statistical Shape Models and Conditional GANs","date":"2020-10-22","arxiv_id":"2010.12011","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-the-effect-of-image-compression","title":"Quantifying the effect of image compression on supervised learning applications in optical microscopy","date":"2020-09-26","arxiv_id":"2009.12570","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-segment-clustered-amoeboid-cells","title":"Learning to segment clustered amoeboid cells from brightfield microscopy via multi-task learning with adaptive weight selection","date":"2020-05-19","arxiv_id":"2005.09372","repositories_listed":0,"syntology":null},{"url":"/paper/evican-a-balanced-dataset-for-algorithm","slug":"evican-a-balanced-dataset-for-algorithm","title":"EVICAN-a balanced dataset for algorithm development in cell and nucleus segmentation","date":"2020-03-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-domain-adaptation-for-cell","title":"Adversarial Domain Adaptation for Cell Segmentation","date":"2020-01-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-method-for-semantic-segmentation-of","title":"Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Image","date":"2020-01-28","arxiv_id":"2001.10188","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-automation-assisted-cervical-cancer","title":"A Novel Automation-Assisted Cervical Cancer Reading Method Based on Convolutional Neural Network","date":"2019-12-14","arxiv_id":"1912.06649","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-multi-task-learning-for","title":"Weakly Supervised Multi-Task Learning for Cell Detection and Segmentation","date":"2019-10-27","arxiv_id":"1910.12326","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-deep-learning-models-for-multi-cell","title":"Comparing Deep Learning Models for Multi-cell Classification in Liquid-based Cervical Cytology Images","date":"2019-10-02","arxiv_id":"1910.00722","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-learning-of-pharmacological-assays","title":"End-to-end learning of pharmacological assays from high-resolution microscopy images","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learn-to-segment-single-cells-with-deep","title":"Learn to segment single cells with deep distance estimator and deep cell detector","date":"2019-04-23","arxiv_id":"1803.10829","repositories_listed":0,"syntology":null},{"url":null,"slug":"algorithms-used-for-the-cell-segmentation","title":"Algorithms used for the Cell Segmentation Benchmark Competition at ISBI 2019 by RWTH-GE","date":"2019-04-15","arxiv_id":"1904.06890","repositories_listed":0,"syntology":null},{"url":null,"slug":"190408503","title":"QANet -- Quality Assurance Network for Image Segmentation","date":"2019-04-09","arxiv_id":"1904.08503","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-approach-for-cell-segmentation","title":"An Efficient Approach for Cell Segmentation in Phase Contrast Microscopy Images","date":"2019-03-31","arxiv_id":"1904.00328","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-neural-circuit-reconstruction-from","title":"Robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks","date":"2018-11-28","arxiv_id":"1811.11356","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-stain-normalization-and-unsupervised","title":"Neural Stain Normalization and Unsupervised Classification of Cell Nuclei in Histopathological Breast Cancer Images","date":"2018-11-09","arxiv_id":"1811.03815","repositories_listed":0,"syntology":null},{"url":null,"slug":"algorithms-for-screening-of-cervical-cancer-a","title":"Algorithms for screening of Cervical Cancer: A chronological review","date":"2018-11-02","arxiv_id":"1811.00849","repositories_listed":0,"syntology":null},{"url":null,"slug":"cnn-based-preprocessing-to-optimize-watershed","title":"CNN-based Preprocessing to Optimize Watershed-based Cell Segmentation in 3D Confocal Microscopy Images","date":"2018-10-16","arxiv_id":"1810.06933","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-generative-refinement-adversarial","title":"Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation","date":"2018-10-09","arxiv_id":"1810.03871","repositories_listed":0,"syntology":null},{"url":"/paper/panoptic-segmentation-with-an-end-to-end-cell","slug":"panoptic-segmentation-with-an-end-to-end-cell","title":"Panoptic Segmentation with an End-to-End Cell R-CNN for Pathology Image Analysis","date":"2018-09-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nuclei-detection-using-mixture-density","title":"Nuclei Detection Using Mixture Density Networks","date":"2018-08-22","arxiv_id":"1808.08279","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-microscopy-data-for-finding","title":"Segmentation of Microscopy Data for finding Nuclei in Divergent Images","date":"2018-08-19","arxiv_id":"1808.06914","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepsdcs-dissecting-cancer-proliferation","title":"DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide images","date":"2018-06-28","arxiv_id":"1806.10850","repositories_listed":0,"syntology":null},{"url":null,"slug":"three-dimensional-gpu-accelerated-active","title":"Three-Dimensional GPU-Accelerated Active Contours for Automated Localization of Cells in Large Images","date":"2018-04-17","arxiv_id":"1804.06304","repositories_listed":0,"syntology":null},{"url":null,"slug":"segment3d-a-web-based-application-for","title":"SEGMENT3D: A Web-based Application for Collaborative Segmentation of 3D images used in the Shoot Apical Meristem","date":"2017-10-26","arxiv_id":"1710.09933","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-segmentation-and-classification-for","title":"Image Segmentation and Classification for Sickle Cell Disease using Deformable U-Net","date":"2017-10-23","arxiv_id":"1710.08149","repositories_listed":0,"syntology":null},{"url":null,"slug":"cell-segmentation-in-3d-confocal-images-using","title":"Cell Segmentation in 3D Confocal Images using Supervoxel Merge-Forests with CNN-based Hypothesis Selection","date":"2017-10-18","arxiv_id":"1710.06608","repositories_listed":0,"syntology":null},{"url":null,"slug":"shadho-massively-scalable-hardware-aware","title":"SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization","date":"2017-07-05","arxiv_id":"1707.01428","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-candidate-multi-cut-for-cell-segmentation","title":"The Candidate Multi-Cut for Cell Segmentation","date":"2017-07-04","arxiv_id":"1707.00907","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstructing-the-forest-of-lineage-trees-of","title":"Reconstructing the Forest of Lineage Trees of Diverse Bacterial Communities Using Bio-inspired Image Analysis","date":"2017-06-22","arxiv_id":"1706.07359","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-pose-and-cell-segmentation-using","title":"Efficient Pose and Cell Segmentation using Column Generation","date":"2016-12-01","arxiv_id":"1612.00437","repositories_listed":0,"syntology":null},{"url":null,"slug":"cell-segmentation-with-random-ferns-and-graph","title":"Cell segmentation with random ferns and graph-cuts","date":"2016-02-17","arxiv_id":"1602.05439","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-segmentation-of-overlapping","title":"Unsupervised Segmentation of Overlapping Cervical Cell Cytoplasm","date":"2015-05-21","arxiv_id":"1505.05601","repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-clustered-nuclei-with-shape","title":"Segmentation of Clustered Nuclei With Shape Markers and Marking Function","date":"2009-04-15","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"de9dbafbef2b8550f9072a1e19320de6dd9bd27497cb5f400bbade58cc0ec688","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}