CellTrack R-CNN: A Novel End-To-End Deep Neural Network for Cell Segmentation and Tracking in Microscopy Images
This work addresses the need for accurate cell analysis in biology and medicine, representing an incremental improvement by combining existing techniques into a unified framework.
The authors tackled the problem of cell segmentation and tracking in microscopy images by proposing an end-to-end deep learning framework that integrates instance segmentation with a Siamese network and spatial information, achieving state-of-the-art performance on the DeepCell benchmark dataset.
Cell segmentation and tracking in microscopy images are of great significance to new discoveries in biology and medicine. In this study, we propose a novel approach to combine cell segmentation and cell tracking into a unified end-to-end deep learning based framework, where cell detection and segmentation are performed with a current instance segmentation pipeline and cell tracking is implemented by integrating Siamese Network with the pipeline. Besides, tracking performance is improved by incorporating spatial information into the network and fusing spatial and visual prediction. Our approach was evaluated on the DeepCell benchmark dataset. Despite being simple and efficient, our method outperforms state-of-the-art algorithms in terms of both cell segmentation and cell tracking accuracies.