Three dimensional tracking of exploratory behavior of barnacle cyprids using stereoscopy.

Surface exploration is a key step in the colonization of surfaces by sessile marine biofoulers. As many biofouling organisms can delay settlement until a suitable surface is encountered, colonization can comprise surface exploration and intermittent swimming. As such, the process is best followed in three dimensions. Here we present a low-cost transportable stereoscopic system consisting of two consumer camcorders. We apply this novel apparatus to behavioral analysis of barnacle larvae (≈800 µm length) during surface exploration and extract and analyze the three-dimensional patterns of movement. The resolution of the system and the accuracy of position determination are characterized. As a first practical result, three-dimensional swimming trajectories of the cypris larva of the barnacle Semibalanus balanoides are recorded in the vicinity of a glass surface and close to PEG2000-OH and C(11)NMe(3)(+)Cl(-) terminated self-assembled monolayers. Although less frequently used in biofouling experiments due to its short reproductive season, the selected model species [Marechal and Hellio (2011), Int Biodeterior Biodegrad, 65(1):92-101] has been used following a number of recent investigations on the settlement behavior on chemically different surfaces [Aldred et al. (2011), ACS Appl Mater Interfaces, 3(6):2085-2091]. Experiments were scheduled to match the availability of cyprids off the north east coast of England so that natural material could be used. In order to demonstrate the biological applicability of the system, analysis of parameters such as swimming direction, swimming velocity and swimming angle are performed.

Compensating motion artifacts of 3D in vivo SD-OCT scans.

We propose a probabilistic approach for compensating motion artifacts in 3D in vivo SD-OCT (spectral-domain optical coherence tomography) tomographs. Subject movement causing axial image shifting is a major problem for in vivo imaging. Our technique is applied to analyze the tissue at percutaneous implants recorded with SD-OCT in 3D. The key challenge is to distinguish between motion and the natural 3D spatial structure of the scanned subject. To achieve this, the motion estimation problem is formulated as a conditional random field (CRF). For efficient inference, the CRF is approximated by a Gaussian Markov random field. The method is verified on synthetic datasets and applied on noisy in vivo recordings showing significant reduction of motion artifacts while preserving the tissue geometry.