Improving Microscopy Image Segmentation with Object Detection

Abstract

Automated analysis of microscopy images is an essential part of modern biological research. Recent advances in deep learning have greatly improved its quality and helped decrease the amount of time-consuming manual work during the experiments. Biologists are interested not only in the accurate detection of various objects (whole cells, cell organelles, tissue structures, etc.) but also in the high-quality segmentation of their shape. In this work, we address the problem of obtaining realistic instance segmentation masks from images with high object density. We show that combining segmentation and detection methods into a single image analysis pipeline helps efficiently separate overlapping objects and improves the segmentation quality. To reduce the complexity of this pipeline, we propose a novel CenterUNet multi-task neural network architecture that simultaneously performs object detection and semantic segmentation. We evaluate the performance of this architecture across several microscopy image domains and conduct a thorough ablation study to identify the necessary and sufficient combination of detection subtasks to solve the segmentation problem. We believe that the results of our research provide valuable insights and can help individual practitioners as well as the image analysis industry. Our developed model may improve microscopy image segmentation pipelines at virtually zero computational cost and little integration efforts.