# Crowd Sourcing Image Segmentation with iaSTAPLE

## TL;DR

This paper introduces iaSTAPLE, a novel label fusion method that combines crowd-sourced annotations with an image segmentation model to improve accuracy in epithelial cell segmentation from microscopy images.

## Contribution

The paper presents iaSTAPLE, a new image-aware label fusion technique that integrates image context into the crowd annotation fusion process, outperforming existing methods like STAPLE.

## Key findings

- iaSTAPLE achieves 99% segmentation accuracy compared to expert annotations.
- It outperforms STAPLE in both segmentation accuracy and worker performance estimation.
- The method effectively integrates crowd annotations with image data for improved segmentation.

## Abstract

We propose a novel label fusion technique as well as a crowdsourcing protocol to efficiently obtain accurate epithelial cell segmentations from non-expert crowd workers. Our label fusion technique simultaneously estimates the true segmentation, the performance levels of individual crowd workers, and an image segmentation model in the form of a pairwise Markov random field. We term our approach image-aware STAPLE (iaSTAPLE) since our image segmentation model seamlessly integrates into the well-known and widely used STAPLE approach. In an evaluation on a light microscopy dataset containing more than 5000 membrane labeled epithelial cells of a fly wing, we show that iaSTAPLE outperforms STAPLE in terms of segmentation accuracy as well as in terms of the accuracy of estimated crowd worker performance levels, and is able to correctly segment 99% of all cells when compared to expert segmentations. These results show that iaSTAPLE is a highly useful tool for crowd sourcing image segmentation.