mlsanityData to 14 Oct 2026

The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

arXiv 2308.05864 ↗First posted 10 Aug 2023eess.IV cs.CV cs.LGcited by 39

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Abstract (arXiv)

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.

Key facts. Cited by 39 papers in the map (data through 14 Oct 2026). Builds on 29 of its 75 references that are in the map. First posted on arXiv 10 Aug 2023.

Builds on

29 of 75 references.

References · 29

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  5. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
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  9. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
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17 more
  1. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,368
  2. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,330
  3. 2018HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Imagescited by 388
  4. 2019Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentationcited by 260
  5. 2023Segment Anything in Medical Imagescited by 227
  6. 2022Metrics reloaded: Recommendations for image analysis validationcited by 195
  7. 2018Cell Detection with Star-convex Polygonscited by 183
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  10. 2022Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectorscited by 69
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  12. 2019Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentationcited by 55
  13. 2023CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Countingcited by 33
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  15. 2021Contour Proposal Networks for Biomedical Instance Segmentationcited by 13
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  17. 2019J Regularization Improves Imbalanced Multiclass Segmentationcited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 17; 2025: 11; 2026: 10.

39 papers.

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  1. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentationcited by 419
  2. 2024Swin-UMamba: Mamba-based UNet with ImageNet-based pretrainingcited by 179
  3. 2024MMedAgent: Learning to Use Medical Tools with Multi-modal Agentcited by 109
  4. 2024Vision Mamba: A Comprehensive Survey and Taxonomycited by 54
  5. 2024Segment Anything in Medical Images and Videos: Benchmark and Deploymentcited by 42
  6. 2024Biomedical SAM 2: Segment Anything in Biomedical Images and Videoscited by 14
  7. 2024xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpartcited by 12
  8. 2024ViM-UNet: Vision Mamba for Biomedical Segmentationcited by 11
  9. 2024Multimodal Data Integration for Precision Oncology: Challenges and Future Directionscited by 10
  10. 2024Cell Graph Transformer for Nuclei Classificationcited by 9
  11. 2024LoG-VMamba: Local-Global Vision Mamba for Medical Image Segmentationcited by 9
  12. 2024HoloHisto: End-to-end Gigapixel WSI Segmentation with 4K Resolution Sequential Tokenizationcited by 8
27 more
  1. 2025SAM3-Adapter: Efficient Adaptation of Segment Anything 3 for Camouflage Object Segmentation, Shadow Detection, and Medical Image Segmentationcited by 7
  2. 2024CellSeg1: Robust Cell Segmentation with One Training Imagecited by 6
  3. 2024TTT-Unet: Enhancing U-Net with Test-Time Training Layers for Biomedical Image Segmentationcited by 3
  4. 2025Enhancing Feature Fusion of U-like Networks with Dynamic Skip Connectionscited by 3
  5. 2023Defining the boundaries: challenges and advances in identifying cells in microscopy imagescited by 2
  6. 2024UniCell: Universal Cell Nucleus Classification via Prompt Learningcited by 1
  7. 2025PyUAT: Open-source Python framework for efficient and scalable cell trackingcited by 1
  8. 2025Fast Sphericity and Roundness approximation in 2D and 3D using Local Thicknesscited by 1
  9. 2025MedVKAN: Efficient Feature Extraction with Mamba and KAN for Medical Image Segmentationcited by 1
  10. 2025SEMA: a Scalable and Efficient Mamba like Attention via Token Localization and Averagingcited by 1
  11. 2025NOA: a versatile, extensible tool for AI-based organoid analysiscited by 1
  12. 2024CHOTA: A Higher Order Accuracy Metric for Cell Trackingcited by 0
  13. 2024CellPilot: A unified approach to automatic and interactive segmentation in histopathologycited by 0
  14. 2025From Claims to Evidence: A Unified Framework and Critical Analysis of CNN vs. Transformer vs. Mamba in Medical Image Segmentationcited by 0
  15. 2025SAMJ: Fast Image Annotation on ImageJ/Fiji via Segment Anything Modelcited by 0
  16. 2025Data Efficiency and Transfer Robustness in Biomedical Image Segmentation: A Study of Redundancy and Forgetting with Cellposecited by 0
  17. 2025Simple Agents Outperform Experts in Biomedical Imaging Workflow Optimizationcited by 0
  18. 2026Cell Behavior Video Classification Challenge, a benchmark for computer vision methods in time-lapse microscopycited by 0
  19. 2026Personalized Cell Segmentation: Benchmark and Framework for Reference-Guided Cell Type Segmentationcited by 0
  20. 2026UCell: rethinking generalizability and scaling of bio-medical vision modelscited by 0
  21. 2026Probing Intrinsic Medical Task Relationships: A Contrastive Learning Perspectivecited by 0
  22. 2026Attention Is not Everything: Efficient Alternatives for Visioncited by 0
  23. 2026USEMA: a Scalable Efficient Mamba Like Attention for Medical Image Segmentationcited by 0
  24. 2026MicroscopyMatching: Towards a Ready-to-use Framework for Microscopy Image Analysis in Diverse Conditionscited by 0
  25. 2026MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentationcited by 0
  26. 2026HASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopycited by 0
  27. 2026QCell: Recombining and Aligning Cell Queries for Overlapping Instance Segmentationcited by 0