FMPD: Freshwater Microscopy Phytoplankton Dataset

FMPD (Freshwater Microscopy Phytoplankton Dataset) is a set of multi-specimen microscopy images of freshwater phytoplankton. These images have been captured with fixed settings, equal for each image, including illumination, focal point and magnification. The dataset contains 293 images from water sampled at lake of Doniños (Ferrol, Galicia, Spain) (UTM 555593 X, 4815672 Y; Datum ETRS89) on multiple visits throughout the year. This ensures seasonal representability.

The phytoplankton sample was concentrated by filtering volume of 0.5 L through GF/F glass fiber filters and was then resuspended in 50 mL. Phytoplankton samples were preserved using 5% (v/v) glutaraldehyde, because it is efficient at preserving both cellular structures and pigment. The fixed sample was stored in the dark at constant temperature (10ºC) until analysis. The phytoplankton sample was homogenised for 2 min prior to microscopic examination. In addition, the sample was subjected to vacuum for one minute to break the vacuoles of some cyanobacterial taxa and prevent them from floating. Aliquots of the phytoplankton sample with a total volume of 1 mL were examined under light microscopy using a Nikon Eclipse E600 equipped with an E-Plan 10× objective (N.A. 0.25). Light microscopy images were taken with an AxioCam ICc5 Zeiss digital camera, maintaining the same illumination and focus throughout the image acquisition process and following regular transects until the entire surface of the sample was covered.

The FMPD can be downloaded from here. Please send an email to: and you will receive an authentication password to access the dataset. This is intended for statistical purposes only, no private data or fee is required. Additionally, the dataset is also hosted on Zenodo for ease of downloading. The data is released for non-comercial academic or research purposes only, subject to attribution through citation of the following papers:

• J. Figueroa, D. Rivas-Villar, J. Rouco, J. Novo, "Phytoplankton detection and recognition in freshwater digital microscopy images using deep learning object detectors", Heliyon, 2024.
• D. Rivas-Villar, J. Rouco, M. G. Penedo, R. Carballeira, J. Novo, "Fully automatic detection and classification of phytoplankton specimens in digital microscopy images", Computer Methods and Programs in Biomedicine, 200, 105923, 2021

Redistribution of the whole dataset, sustantial parts of it, or derivatives, is explicitly disallowed. Please also consider the citation of any of the other related papers from the dataset authors.

Main publications

• J. Figueroa, D. Rivas-Villar, J. Rouco, J. Novo, "Phytoplankton detection and recognition in freshwater digital microscopy images using deep learning object detectors", Heliyon, 2024.
  [Abstract] [PDF]
  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. 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.
• D. Rivas-Villar, J. Morano, J. Rouco, J. Novo, "Deep Features-based approaches for Phytoplankton Classification in Microscopy Images", Lecture Notes in Computer Science: Computer Aided Systems Theory, Revised Selected Papers, EUROCAST 2022, Las Palmas de Gran Canaria, Spain, 2023.
  [PDF] [+Info.]
• D. Rivas-Villar, J. Rouco, M. G. Penedo, R. Carballeira, J. Novo, "Fully automatic detection and classification of phytoplankton specimens in digital microscopy images", Computer Methods and Programs in Biomedicine, 200, 105923, 2021.
  [Abstract] [PDF]
  Background and objective The proliferation of toxin-producing phytoplankton species can compromise the quality of the water sources. This contamination is difficult to detect, and consequently to be neutralised, since normal water purifica- tion techniques are ineffective. Currently, the water analyses about phytoplankton are commonly performed by the specialists with manual routine analyses, which represents a major limitation. The adequate identification and classification of phytoplankton specimens requires intensive training and expertise. Additionally, the performed analysis involves a lengthy process that exhibits serious problems of reliability and repeatability as inter-expert agreement is not always reached. Considering all those factors, the automatization of these analyses is, therefore,highly desirable to reduce the workload of the specialists and facilitate the process. Methods This manuscript proposes a novel fully automatic methodology to per- form phytoplankton analyses in digital microscopy images of several water sam- ples which are taken with a regular light microscope. In particular, the proposed method analyses conventional microscopy images containing multiple specimens. The system is able to detect and segment the different existing phytoplankton specimens with high variability in terms of visual appearances, even merging them into colonies, when necessary, through the exploitation of colour features. More- over, the system is capable of differentiating them from other similar objects like zooplankton, detritus, mineral particles, among others, and then classify the spec- imens into a defined target of species of interest using a machine learning-based approach. Results The proposed system provided satisfactory and accurate results in every step. The detection step provided a FNR of 0.4%. Phytoplankton detection, that is, differentiating true phytoplankton from similar objects (zooplankton, minerals, etc.), provided a result of 84.07% of precision at 90% of recall. The target species classification, reported an overall accuracy of 87.50%. The recall levels for each species are, 81.82% for W. naegeliana, 57.15% for A. spiroides, 85.71% for D. sociale and 95% for the ”Other” group, a set of relevant toxic and interesting species widely spread over the samples. Conclusions The proposed methodology provided accurate results in all the de- signed steps given the complexity of the problem, particularly in terms of spec- imen identification, phytoplankton differentiation as well as the classification of the defined target species. Therefore, this fully automatic system represents a robust and consistent tool to aid the specialists in the analysis of the quality of the water sources and potability.
• D. Rivas-Villar, J. Rouco, M. G. Penedo, R. Carballeira, J. Novo, "Automatic Detection of Fresh-water Phytoplankton Specimens in Conventional Microscopy Images", Sensors, 20, 2020.
  [Abstract] [PDF]
  Water safety and quality can be compromised by the proliferation of toxin-producing phytoplankton species, requiring continuous monitoring of water sources. This analysis involves the identification and counting of these species which requires broad experience and knowledge. The automatization of these tasks is highly desirable as it would release the experts from tedious work, eliminate subjective factors, and improve repeatability. Thus, in this work, we propose a novel fully automatic methodology to analyse phytoplankton in digital water sample images acquired using regular microscopes. In particular, the method detects and segments the existent phytoplankton specimens, also differentiating them from other image objects like minerals, bubbles or detritus. Our preliminary experiments demonstrate that the proposed method provides satisfactory and accurate results.