Figueroa Jorge, Rivas-Villar David, Rouco José, Novo Jorge
Centro de investigacion CITIC, Universidade da Coruña, 15071 A Coruña, Spain.
Grupo VARPA, Instituto de Investigacion Biomédica de A Coruña (INIBIC), Universidade da Coruna, 15006 A Coruña, Spain.
Heliyon. 2024 Jan 30;10(3):e25367. doi: 10.1016/j.heliyon.2024.e25367. eCollection 2024 Feb 15.
Water quality can be negatively affected by the presence of some toxic phytoplankton species, whose toxins are difficult to remove by conventional purification systems. This creates the need for periodic analyses, which are nowadays manually performed by experts. These labor-intensive processes are affected by subjectivity and expertise, causing unreliability. Some automatic systems have been proposed to address these limitations. However, most of them are based on classical image processing pipelines with not easily scalable designs. In this context, deep learning techniques are more adequate for the detection and recognition of phytoplankton specimens in multi-specimen microscopy images, as they integrate both tasks in a single end-to-end trainable module that is able to automatize the adaption to such a complex domain. In this work, we explore the use of two different object detectors: Faster R-CNN and RetinaNet, from the one-stage and two-stage paradigms respectively. We use a dataset composed of multi-specimen microscopy images captured using a systematic protocol. This allows the use of widely available optical microscopes, also avoiding manual adjustments on a per-specimen basis, which would require expert knowledge. We have made our dataset publicly available to improve the reproducibility and to foment the development of new alternatives in the field. The selected Faster R-CNN methodology reaches maximum recall levels of 95.35%, 84.69%, and 79.81%, and precisions of 94.68%, 89.30% and 82.61%, for W. naegeliana, A. spiroides, and D. sociale, respectively. The system is able to adapt to the dataset problems and improves the results overall with respect to the reference state-of-the-art work. In addition, the proposed system improves the automation and abstraction from the domain and simplifies the workflow and adjustment.
某些有毒浮游植物物种的存在会对水质产生负面影响，其毒素难以通过传统净化系统去除。这就需要进行定期分析，目前这些分析由专家手动完成。这些劳动密集型过程受主观性和专业知识的影响，导致结果不可靠。已经提出了一些自动系统来解决这些局限性。然而，它们中的大多数基于经典图像处理管道，设计不易扩展。在这种背景下，深度学习技术更适合于在多样本显微镜图像中检测和识别浮游植物标本，因为它们将这两项任务集成在一个单一的端到端可训练模块中，该模块能够自动适应如此复杂的领域。在这项工作中，我们分别探索了使用来自单阶段和两阶段范式的两种不同目标检测器：Faster R-CNN和RetinaNet。我们使用了一个由使用系统协议捕获的多样本显微镜图像组成的数据集。这允许使用广泛可用的光学显微镜，还避免了逐个样本进行手动调整，而这需要专业知识。我们已将我们的数据集公开，以提高可重复性并促进该领域新替代方法的发展。对于纳氏魏氏藻、螺旋鱼腥藻和群居筒柱藻，所选的Faster R-CNN方法分别达到了95.35%、84.69%和79.81%的最大召回率，以及94.68%、89.30%和82.61%的精度。该系统能够适应数据集问题，并且相对于参考的现有技术工作总体上改善了结果。此外，所提出的系统提高了自动化程度，减少了对领域的依赖，简化了工作流程和调整。
