Weighted average ensemble-based semantic segmentation in biological electron microscopy images

Semantic segmentation of electron microscopy images using deep learning methods is a valuable tool for the detailed analysis of organelles and cell structures. However, these methods require a large amount of labeled ground truth data that is often unavailable. To address this limitation, we present a weighted average ensemble model that can automatically segment biological structures in electron microscopy images when trained with only a small dataset. Thus, we exploit the fact that a combination of diverse base-learners is able to outperform one single segmentation model. Our experiments with seven different biological electron microscopy datasets demonstrate quantitative and qualitative improvements. We show that the Grad-CAM method can be used to interpret and verify the prediction of our model. Compared with a standard U-Net, the performance of our method is superior for all tested datasets. Furthermore, our model leverages a limited number of labeled training data to segment the electron microscopy images and therefore has a high potential for automated biological applications.

Automated summary

Semantic segmentation of electron microscopy images using deep learning methods is a valuable tool for analyzing cell structures. This study introduces a weighted average ensemble model that can automatically segment biological structures in electron microscopy images using a small dataset. The experiments show quantitative and qualitative improvements, and the Grad-CAM method is used for interpretation and verification.