Cell detection and tracking are paramount for bio-analysis. Recent approaches rely on the tracking-by-model evolution paradigm, which usually consists of training end-to-end deep learning models to detect and track the cells on the frames with promising results. However, such methods require extensive amounts of annotated data, which is time-consuming to obtain and often requires specialized annotators. This work proposes a new approach based on the classical tracking-by-detection paradigm that alleviates the requirement of annotated data. More precisely, it approximates the cell shapes as oriented ellipses and then uses generic-purpose oriented object detectors to identify the cells in each frame. We then rely on a global data association algorithm that explores temporal cell similarity using probability distance metrics, considering that the ellipses relate to two-dimensional Gaussian distributions. Our results show that our method can achieve detection and tracking results competitively with state-of-the-art techniques that require considerably more extensive data annotation. Our code is available at: https://github.com/LucasKirsten/Deep-Cell-Tracking-EBB.

翻译：细胞检测与跟踪对生物分析至关重要。现有方法多依赖于基于模型演化的跟踪范式，通常通过端到端深度学习模型在视频帧中检测并跟踪细胞，并取得了良好效果。然而，这类方法需要大量标注数据，获取此类数据耗时且通常需要专业标注人员。本文提出一种基于经典检测跟踪范式的新方法，能显著降低对标注数据的需求。具体而言，我们采用定向椭圆近似细胞形状，进而使用通用定向目标检测器识别每帧中的细胞。随后，利用全局数据关联算法，通过概率距离度量探索细胞间的时间相似性，并将椭圆视为二维高斯分布。实验结果表明，本方法在检测与跟踪性能上与需要更大量数据标注的现有先进技术具有竞争力。相关代码已开源：https://github.com/LucasKirsten/Deep-Cell-Tracking-EBB