Multi-Object Dynamic Memory Network For Cell Tracking in Time-Lapse Microscopy Images
Ran Li, Qi Gang Gao, Karl Rohr · 2021
Cell tracking in time-lapse microscopy images is important to study biological processes. We propose a new multi-object tracking method based on a dynamic memory network and template matching. Cells are detected by a fully convolutional neural network, and multiple dynamic memory units are used to track cells in successive frames. The template is dynamically updated using a long-short term memory with attention to cope with changing cell appearance. Our method includes a motion constraint based on cell motion statistics to improve the robustness. To handle cell mitosis events, a deep mitosis detector is integrated in our tracking method. We evaluated the proposed method on time-lapse microscopy datasets including data from the Cell Tracking Challenge. Experimental results demonstrate that our method yields state-of-the-art results or better results than baseline methods.