Semantic segmentation for fully automated macrofouling analysis on coatings after field exposure.

Biofouling is a major challenge for sustainable shipping, filter membranes, heat exchangers, and medical devices. The development of fouling-resistant coatings requires the evaluation of their effectiveness. Such an evaluation is usually based on the assessment of fouling progression after different exposure times to the target medium (e.g. salt water). The manual assessment of macrofouling requires expert knowledge about local fouling communities due to high variances in phenotypical appearance, has single-image sampling inaccuracies for certain species, and lacks spatial information. Here an approach for automatic image-based macrofouling analysis was presented. A dataset with dense labels prepared from field panel images was made and a convolutional network (adapted U-Net) for the semantic segmentation of different macrofouling classes was proposed. The establishment of macrofouling localization allows for the generation of a successional model which enables the determination of direct surface attachment and in-depth epibiotic studies.

Layer-by-Layer Deposited Hybrid Polymer Coatings Based on Polysaccharides and Zwitterionic Silanes with Marine Antifouling Properties.

Polyelectrolyte multilayer (PEM) assembly is a versatile tool to construct low-fouling coatings. For application in the marine environment, their structure needs to be stabilized by covalent linkage. Here, we introduce an approach for spin coating of silane-based sol-gel chemistries using layer-by-layer assembly of polysaccharide-based hybrid polymer coatings (LBLHPs). The silane sol-gel chemistry allows the films to be cross-linked under water-based and mild reaction conditions. Two different silanes were used for this purpose, a conventional triethoxymethyl silane and a de novo synthesized zwitterionic silane. The polysaccharide-silane hybrid polymer coatings were thoroughly characterized with spectroscopic ellipsometry, water contact angle (WCA) goniometry, attenuated total reflection-Fourier transform infrared spectroscopy, and atomic force microscopy. The coatings showed good stability in seawater, smooth surfaces, a high degree of hydration, and WCAs below or close to the Berg limit. LBLHPs showed low-fouling properties in biological assays against nonspecific protein adsorption, attachment of the diatom Navicula perminuta, and settlement of zoospores of the macroalga Ulva linza.

Fully Convolutional Neural Network for Detection and Counting of Diatoms on Coatings after Short-Term Field Exposure.

While the use of deep learning is a valuable technology for automatic detection systems for medical data and images, the biofouling community is still lacking an analytical tool for the detection and counting of diatoms on samples after short-term field exposure. In this work, a fully convolutional neural network was implemented as a fast and simple approach to detect diatoms on two-channel (fluorescence and phase-contrast) microscopy images by predicting bounding boxes. The developed approach performs well with only a small number of trainable parameters and a F1 score of 0.82. Counting diatoms was evaluated on a data set of 600 microscopy images of three different surface chemistries (hydrophilic and hydrophobic) and is very similar to counting by humans while demanding only a fraction of the analysis time.