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Table 2 Literature review for automated species identification for phytoplankton species identification with machine learning (neural networks), with flow cytometric data or images

From: Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton

  Reference Network Species/classes Species size Parameters included Type of parameter Instrument Accuracy
Flow cytometric data (scatter and fluorescence values) Frankel et al. [19] ANN (Kohonen network/Back-propagation neural networks) 5 Picoplankton, large phytoplankton 5 FSC
488 nm Ex./540–630 nm
514 nm Ex./540–630 nm
488 nm Ex./660–700 nm
514 nm Ex./660–700 nm
EPICS V 92–100%
Balfoort et al. [1] Multilayer feedforward network (NWorks, ANNET, 8) 8 3–3500 µm 6 FSC, SSC, TOF
488 nm Ex./515–600 nm Em
488 nm Ex./650–750 nm Em
633 nm Ex./650–750 nm Em
Optical Plankton Analyzer 90–98%
Boddy et al. [6] Back-propagation neural networks, hierarchical approach 40 3–40 µm 6 FSC (horizontal, vertical), SSC, TOF
488 nm Ex./> 660 nm
488 nm Ex./530–590 nm
EPICS 741 > 70%
Wilkins et al. [51] MLP, RBF 42 Boddy et al. [6] 68–74%
Wilkins et al. [52] RBF ANN 34 3–1000 µm 11 FSC, SSC, TOF
488 nm Ex./Red Em
488 nm Ex./Orange Em
488 nm Ex./Green Em
630 nm Ex./Red Em
Diffraction module: Vertical bar, horizontal bar, outer ring, inner ring
EurOPA 92%
Boddy et al. [5] RBF NN 72 1–45 µm 7 FSC-H, SSC-H, FL1-H, FL2-H, FL3-H, FL3-A, TOF FACSort™ 70–77%
Pulse shape Malkassian et al. [33]   20 n. a. 8 FSC, SSC
FLR (668–734 nm Em.)
FLO (601–668 nm Em.)
FLY (536–601 nm Em.)
Pulse shape descriptors (shape, length and area under the pulse)
CytoSub 78%
Images Gorsky et al. [23]   3 3–43 µm 5 Area, Circularity, Convexity, Length, Perimeter Autonomous Image Analyzer/HIAC
Embleton et al. [17] MLP 4 10–390 µm 74 Area, Circularity, Diameter, Fibrelength, Grey level values (SD, Skewness, Kurtosis), Perimeter Microscope camera (Sony DXC-930P) 67–93%
Sosik and Olson [45] SVM 15 (natural samples) 10–100 µm 22 categories/210 elements Size, shape, symmetry, Texture characteristics, Diffraction, Co-occurence FlowCytobot 68–99%
Blaschko et al. [4] SVM 13 classes n. a. 780 features Simple shape, moments, contour, differential, texture FlowCam 71%
Correa et al. [9] CNN 19 classes n.a. FlowCam 89%
Rodenacker et al. [42] DT, LDA 23 n. a. 5 Shape, significant points, principal components, contour, fourier descriptor, extinction, Shape moments, colorimetry, fluorimetry Inverse microscope 76%
Chen et al. [8] CNN, PCA + SVM 1 n. a. 16 Diameter, tight area, perimeter, circularity, major axis, orientation, loose area, median radius, opd, refractive index, absorption, scattering TS-QPI < 85%
Li et al. [31] CNN 9 n. a. Mueller matrix microscope 97%
Pedraza et al. [37] CNN 80 n. a. Microscopy 99%
This study CNN 9 1–90 µm ImageStream®X MK II 97%
  1. The respective network, species or classes, species size, parameter number and parameter type, instruments or technique and the respective accuracy is provided. For some studies it was not possible to get details from the text about investigated cell size range
  2. TOF Time of flight, MLP Multilayer Perceptron/backpropagation network, RBF radial basis function, SVM support vector machine, TS-QPI Time stretch quantitative phase imaging, OPD optical path length difference, n. a. not available, Picoplankton 0.2–2 µm, Nanoplankton 2–20 µm, Microplankton 20–200 µm, Macroplankton 200–2000 µm