Cell Tracking-by-detection using Elliptical Bounding Boxes

• URL: http://arxiv.org/abs/2310.04895v2
• Date: Wed, 11 Oct 2023 05:59:53 GMT
• Title: Cell Tracking-by-detection using Elliptical Bounding Boxes
• Authors: Lucas N. Kirsten, Cl\'audio R. Jung
• Abstract summary: This work proposes a new approach based on the classical tracking-by-detection paradigm. It approximates the cell shapes as oriented ellipses and then uses generic-purpose oriented object detectors to identify the cells in each frame. Our results show that our method can achieve detection and tracking results competitively with state-of-the-art techniques.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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