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Defining the boundaries: challenges and advances in identifying cells in microscopy images

Nodar Gogoberidze, Beth A. Cimini

TL;DR

Segmentation in microscopy is pivotal for quantitative analysis but faces variability across cell types and imaging modalities. The paper surveys progress from classical computer vision to deep-learning approaches, highlighting specialist networks (e.g., StarDist, Cellpose, nucleAIzer, Mesmer) and the push toward foundation-model-like segmentation with universal applicability. It outlines key ingredients for progress: diverse datasets, robust benchmarks, FAIR access, and efficient, user-friendly tooling that lowers the time to scientific insight. By advocating standardized reporting, containerized workflows, and interoperable interfaces, the work argues that democratizing access to high-accuracy segmentation will accelerate discoveries in single-cell biology.

Abstract

Segmentation, or the outlining of objects within images, is a critical step in the measurement and analysis of cells within microscopy images. While improvements continue to be made in tools that rely on classical methods for segmentation, deep learning-based tools increasingly dominate advances in the technology. Specialist models such as Cellpose continue to improve in accuracy and user-friendliness, and segmentation challenges such as the Multi-Modality Cell Segmentation Challenge continue to push innovation in accuracy across widely-varying test data as well as efficiency and usability. Increased attention on documentation, sharing, and evaluation standards are leading to increased user-friendliness and acceleration towards the goal of a truly universal method.

Defining the boundaries: challenges and advances in identifying cells in microscopy images

TL;DR

Segmentation in microscopy is pivotal for quantitative analysis but faces variability across cell types and imaging modalities. The paper surveys progress from classical computer vision to deep-learning approaches, highlighting specialist networks (e.g., StarDist, Cellpose, nucleAIzer, Mesmer) and the push toward foundation-model-like segmentation with universal applicability. It outlines key ingredients for progress: diverse datasets, robust benchmarks, FAIR access, and efficient, user-friendly tooling that lowers the time to scientific insight. By advocating standardized reporting, containerized workflows, and interoperable interfaces, the work argues that democratizing access to high-accuracy segmentation will accelerate discoveries in single-cell biology.

Abstract

Segmentation, or the outlining of objects within images, is a critical step in the measurement and analysis of cells within microscopy images. While improvements continue to be made in tools that rely on classical methods for segmentation, deep learning-based tools increasingly dominate advances in the technology. Specialist models such as Cellpose continue to improve in accuracy and user-friendliness, and segmentation challenges such as the Multi-Modality Cell Segmentation Challenge continue to push innovation in accuracy across widely-varying test data as well as efficiency and usability. Increased attention on documentation, sharing, and evaluation standards are leading to increased user-friendliness and acceleration towards the goal of a truly universal method.
Paper Structure (11 sections, 1 figure)

This paper contains 11 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Progress in classical approaches
  3. Lowering time-to-science in deep learning
  4. Current progress towards useful specialist networks
  5. A Vision for the State-Of-The-Art
  6. Dataset Availability
  7. Next Generation Models
  8. Making models FAIR
  9. Making models efficient
  10. Improving tool access and availability
  11. Conclusion

Figures (1)

  • Figure 1: Segmentation and fine tuning process. a) The inference dataset contains a variety of samples; some are similar to the original training data the model was pre-trained on, while others are slightly or very different, leading to low segmentation accuracy. b) New training data matching the characteristics of the full distribution is annotated either manually, through software such as CellProfiler, or a human-in-the-loop model such as Cellpose, and used to fine-tune the model. c) The fine-tuned model produces more accurate segmentations on the inference dataset, which can then be used for downstream tasks.