Author:
Wang Fei,Li Huawei,Yang Wanli,Jin Shidong,Gao Peng
Abstract
AbstractCell tracking is currently a powerful tool in a variety of biomedical research topics. Most cell tracking algorithms follow the tracking by detection paradigm. Detection is critical for subsequent tracking. Unfortunately, very accurate detection is not easy due to many factors like densely populated, low contrast, and possible impurities included. Keeping tracking multiple cells across frames suffers many difficulties, as cells may have similar appearance, they may change their shapes, and nearby cells may interact each other. In this paper, we propose a unified tracking-by-detection framework, where a powerful detector AttentionUnet++, a multimodal extension of the Efficient Convolution Operators algorithm, and an effective data association algorithm are included. Experiments show that the proposed algorithm can outperform many existing cell tracking algorithms.
Funder
Natural Science Foundation of Guangdong
Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Information Systems,Theoretical Computer Science,Software