# Few-Shot Microscopy Image Cell Segmentation

arXiv: https://arxiv.org/abs/2007.01671 · HTML: https://mlsanity.com/paper/2007.01671/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

## Built on by (most cited first, 11 of 11)

- [MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation](https://mlsanity.com/paper/2109.09734/) (2021)
- [Morphological Profiling for Drug Discovery in the Era of Deep Learning](https://mlsanity.com/paper/2312.07899/) (2023)
- [Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation](https://mlsanity.com/paper/2208.02105/) (2022)
- [Knowing What to Label for Few Shot Microscopy Image Cell Segmentation](https://mlsanity.com/paper/2211.10244/) (2022)
- [ParticleAugment: Sampling-Based Data Augmentation](https://mlsanity.com/paper/2106.08693/) (2021)
- [One-shot and Partially-Supervised Cell Image Segmentation Using Small Visual Prompt](https://mlsanity.com/paper/2304.07991/) (2023)
- [CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation](https://mlsanity.com/paper/2310.03981/) (2023)
- [Adaptive Prompt Learning with SAM for Few-shot Scanning Probe Microscope Image Segmentation](https://mlsanity.com/paper/2410.12562/) (2024)
- [HyperBank: A Differentiable Bank of Classical Priors for Few-Shot Spheroid Microscopy Segmentation](https://mlsanity.com/paper/2607.10684/) (2026)
- [Self-mentoring: a new deep learning pipeline to train a self-supervised U-net for few-shot learning of bio-artificial capsule segmentation](https://mlsanity.com/paper/2205.10840/) (2022)
- [SelectNAdapt: Support Set Selection for Few-Shot Domain Adaptation](https://mlsanity.com/paper/2308.04946/) (2023)

## Abstract (arXiv)

Automatic cell segmentation in microscopy images works well with the support of deep neural networks trained with full supervision. Collecting and annotating images, though, is not a sustainable solution for every new microscopy database and cell type. Instead, we assume that we can access a plethora of annotated image data sets from different domains (sources) and a limited number of annotated image data sets from the domain of interest (target), where each domain denotes not only different image appearance but also a different type of cell segmentation problem. We pose this problem as meta-learning where the goal is to learn a generic and adaptable few-shot learning model from the available source domain data sets and cell segmentation tasks. The model can be afterwards fine-tuned on the few annotated images of the target domain that contains different image appearance and different cell type. In our meta-learning training, we propose the combination of three objective functions to segment the cells, move the segmentation results away from the classification boundary using cross-domain tasks, and learn an invariant representation between tasks of the source domains. Our experiments on five public databases show promising results from 1- to 10-shot meta-learning using standard segmentation neural network architectures.