Few-Shot Microscopy Image Cell Segmentation

TL;DR

This paper introduces a meta-learning approach for few-shot cell segmentation in microscopy images, enabling effective adaptation to new domains with limited annotated data, reducing the need for extensive annotation efforts.

Contribution

It proposes a novel meta-learning framework combining three objectives to improve few-shot segmentation across diverse microscopy domains.

Findings

Effective 1- to 10-shot segmentation demonstrated on five datasets.

Meta-learning approach outperforms baseline methods.

Model adapts well to different cell types and image appearances.

Abstract

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.