An Ensemble Learning Method for Segmentation Fusion

Abstract

The segmentation of cells present in microscope images is an essential step in many tasks, including determining protein concentration and analysis of gene expression per cell. In single-cell genomics studies, cell segmentations are vital to assess the genetic makeup of individual cells and their relative spatial location. Several methods and tools have been developed to offer robust segmentation, with deep learning models currently being the most promising solutions. As an alternative to developing another cell segmentation targeted model, we propose a learning ensemble strategy that aggregates many independent candidate segmentations of the same image to produce a single consensus segmentation. We are particularly interested in learning how to ensemble crowdsource image segmentations created by experts and non-experts in laboratories and data houses. We compare our trained ensemble model with other fusion methods adopted by the biomedical community and assess the robustness of the results on three aspects: fusion with outliers, missing data, and synthetic deformations. Our approach outperforms these methods in efficiency and quality, especially when there is a high disagreement among candidate segmentations of the same image.