CellTrack R-CNN: A Novel End-To-End Deep Neural Network for Cell Segmentation and Tracking in Microscopy Images

• URL: http://arxiv.org/abs/2102.10377v1
• Date: Sat, 20 Feb 2021 15:55:40 GMT
• Title: CellTrack R-CNN: A Novel End-To-End Deep Neural Network for Cell Segmentation and Tracking in Microscopy Images
• Authors: Yuqian Chen, Yang Song, Chaoyi Zhang, Fan Zhang, Lauren O'Donnell, Wojciech Chrzanowski, Weidong Cai
• Abstract summary: We propose a novel approach to combine cell segmentation and cell tracking into a unified end-to-end deep learning based framework. Our method outperforms state-of-the-art algorithms in terms of both cell segmentation and cell tracking accuracies.
• Score: 21.747994390120105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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