IEEE Cybermatics第二届国际 “Vision Meets Algae”挑战赛和研讨会

微藻作为重要的自然资源，在海洋环境、生物医学研究、清洁能源和食品工程等多个领域有着广泛的应用。监测水体中微藻的丰度和种类组成可以帮助我们识别潜在的环境问题，如富营养化、水质与生态平衡的变化等，这对于生态评估、预警系统的建立以及海洋生态系统恢复的实施具有重要意义。微藻研究中的一个主要挑战在于快速识别和定量多样化和可变的微藻样本。目前，研究人员主要依赖于微藻细胞的显微检查。然而，这种方法往往存在低通量和样品损伤的问题，结果可能忽视了细胞之间的潜在异质性。

针对以上问题，微流控芯片技术是一个具有前景的解决方案。微流控芯片以微型化、集成化、高灵敏度和低成本为特点，逐渐成为实时细胞分析和微生物监测的理想平台。然而，现有的微流控平台上的细胞检测模型有局限性，难以同时实现细胞的多分类和对相似大小但状态不同的细胞的准确检测。这在微藻样本中尤其明显，由于种类多样，同一物种中的细胞大小变化，以及不同物种之间的大小差异显著，这构成了一个独特的挑战。因此，迫切需要开发一种无标签、高通量、多分类、多尺度的微藻细胞检测方法，以满足微藻研究的实际需求。这一发展将有助于推动微藻领域的技术创新，并为未来的生态保护和资源管理提供更可靠的工具。

为了应对高通量藻类细胞检测的挑战，我们组织了第二届国际"Vision Meets Algae"（VisAlgae）挑战赛与研讨会。VisAlgae 2023将与IEEE Cybermatics大会一同举行，专注于藻类研究和计算机视觉技术的领域交叉与应用。我们在高通量微流控平台上进行了实验，在不同视场和成像条件下采集微藻细胞的动态视频数据。实验对象包括六种微藻细胞类型：扁藻、小球藻、杜氏盐藻、虫黄藻、紫球藻和雨生红球藻。这些细胞在大小上具有显著差异，存在极小的目标，以及在某些条件下会出现的运动模糊和失焦。我们的任务是解决高通量藻类细胞检测的实际挑战，开发针对性的目标检测算法，以克服诸如检测小目标、处理多尺度、处理运动模糊和处理复杂背景等问题，并最大化检测精度。

As an important natural resource, microalgae have widespread applications across multiple fields including marine environments, biomedical research, clean energy, and food engineering. Monitoring the abundance and species composition of microalgae in water bodies helps identify potential environmental issues such as eutrophication, changes in water quality and ecological balance, which is of great significance for ecological assessment, the establishment of early warning systems, and the implementation of marine ecosystem restoration.
A major challenge in microalgae research lies in the rapid identification and quantification of diverse and variable microalgae samples. Currently, researchers mainly rely on microscopic examination of microalgal cells. However, this approach often suffers from low throughput and sample damage, and the results may overlook the potential heterogeneity between cells.
To address these issues, microfluidic chip technology is a promising solution. Characterized by miniaturization, integration, high sensitivity, and low cost, microfluidic chips have gradually become ideal platforms for real-time cell analysis and microbial monitoring. However, existing cell detection models on microfluidic platforms have limitations, making it difficult to simultaneously achieve multi-classification of cells and accurate detection of cells with similar sizes but different states. This is particularly prominent in microalgae samples: due to the diversity of species, changes in cell size within the same species, and significant size differences between different species, this constitutes a unique challenge. Therefore, there is an urgent need to develop a label-free, high-throughput, multi-classification, and multi-scale microalgal cell detection method to meet the practical needs of microalgae research. This development will help promote technological innovation in the microalgae field and provide more reliable tools for future ecological conservation and resource management.
To address the challenges of high-throughput algal cell detection, we organized the 2nd International "Vision Meets Algae" (VisAlgae) Challenge and Workshop. VisAlgae 2023 will be held in conjunction with the IEEE Cybermatics Conference, focusing on the interdisciplinary intersection and applications of algal research and computer vision technology. We conducted experiments on a high-throughput microfluidic platform, collecting dynamic video data of microalgal cells under different fields of view and imaging conditions. The experimental subjects include six types of microalgal cells: Platymonas, Chlorella, Dunaliella salina, Zooxanthellae, Porphyridium, and Haematococcus pluvialis. These cells have significant differences in size, include extremely small targets, and may suffer from motion blur and defocusing under certain conditions. Our task is to address the practical challenges of high-throughput algal cell detection, develop targeted object detection algorithms to overcome issues such as small target detection, multi-scale processing, motion blur handling, and complex background processing, and maximize detection accuracy.

搜集汇总

背景与挑战

背景概述

该数据集来自IEEE Cybermatics第二届国际“Vision Meets Algae”挑战赛，专注于微藻细胞的高通量检测，旨在解决小目标、多尺度、运动模糊和复杂背景等计算机视觉挑战。数据集包含700张训练图像和300张测试图像，覆盖六种微藻类别（如扁藻、小球藻等），标注采用YOLO格式，适用于目标检测算法的开发与评估。