Meta-Learning for Segmention of In Situ Hybriization Gene Expression Images

Charissa Poon, Michal Byra, Tomomi Shimogori, Henrik Skibbe

Keywords: meta-learning, segmentation, gene expression

Abstract: Segmentation of biomedical images is often ambiguous and complicated by noise, varying contrasts, and imaging artifacts. We address the challenge of segmenting images of brain tissue in which gene expression has been localized using in situ hybridization. Since gene expression patterns differ widely between genes, it can be difficult to correctly discriminate pixels positive for gene expression. In testing different segmentation networks, we observed that each network had its own trade-offs between sensitivity and precision. To exploit the benefits of all trained networks, we developed a meta-network that learns to combine multiple segmentation maps from diverse segmentation architectures to generate a final segmentation that best matches the ground-truth label. In our experiments, the meta-network outperforms ensembles that simply average segmentation maps.

Submission Number: 31