Plankton classification with high-throughput submersible holographic microscopy and transfer learning

Abstract

Background: Plankton are foundational to marine food webs and an important feature for characterizing ocean health. Recent developments in quantitative imaging devices provide in-flow high-throughput sampling from bulk volumes-opening new ecological challenges exploring microbial eukaryotic variation and diversity, alongside technical hurdles to automate classification from large datasets. However, a limited number of deployable imaging instruments have been coupled with the most prominent classification algorithms-effectively limiting the extraction of curated observations from field deployments. Holography offers relatively simple coherent microscopy designs with non-intrusive 3-D image information, and rapid frame rates that support data-driven plankton imaging tasks. Classification benchmarks across different domains have been set with transfer learning approaches, focused on repurposing pre-trained, state-of-the-art deep learning models as classifiers to learn new image features without protracted model training times. Combining the data production of holography, digital image processing, and computer vision could improve in-situ monitoring of plankton communities and contribute to sampling the diversity of microbial eukaryotes.

Results: Here we use a light and portable digital in-line holographic microscope (The HoloSea) with maximum optical resolution of 1.5 μm, intensity-based object detection through a volume, and four different pre-trained convolutional neural networks to classify > 3800 micro-mesoplankton (> 20 μm) images across 19 classes. The maximum classifier performance was quickly achieved for each convolutional neural network during training and reached F1-scores > 89%. Taking classification further, we show that off-the-shelf classifiers perform strongly across every decision threshold for ranking a majority of the plankton classes.

Conclusion: These results show compelling baselines for classifying holographic plankton images, both rare and plentiful, including several dinoflagellate and diatom groups. These results also support a broader potential for deployable holographic microscopes to sample diverse microbial eukaryotic communities, and its use for high-throughput plankton monitoring.

Keywords: Classification workflow; Convolutional neural networks; Deep learning; Deployable microscope; High-throughput imaging; Holographic microscopy; Plankton.

Fig. 1

The workflow for imaging, detecting, and selecting in-focus objects. Volumes are recorded in the microscopes sample space and the interference pattern is reconstructed to create a hologram. Plankton objects are first detected as ROIs across 300 reconstructed planes (i.e., z-distances) of a hologram corresponding to the 15 mm sample space. The plane containing an in-focus object is calculated via autocorrelation and Vollath’s F4 algorithm

Fig. 2

Amplitude images reconstructed and detected from specific focal planes for each plankton class. From top left to lower right: Alexandrium tamarense, Ceratium fusus, Ceratium lineatum, Ceratium longpipes, Ceratium sp., Chaetoceros socialis, Chaetoceros straight, Chaetoceros sp., Crustacean, Dictyocha speculum, Melosira octagona, Parvicorbicula socialis, Prorocentrum micans, Pseudo-nitchzia arctica, Rhizosolenia setigera, Rods, Skeletonema costatum, Tintinnid. All images are segmented to 128 × 128 pixels and scale bars represent 50 µm