SIGNIFICANCE: Three-dimensional photoacoustic imaging (PAM) has emerged as a promising technique for non-invasive label-free visualization and characterization of biological tissues with high spatial resolution and functional contrast. AIM: The application of PAM and ultrasound as a microscopy technique of study for Atlantic salmon skin is presented here. APPROACH: A custom ultrasound and photoacoustic experimental setup was used for conducting this experiment with a sample preparation method where the salmon skin is embedded in agarose and lifted from the bottom of the petridish. RESULTS: The results of C-scan, B-scan, and overlayed images of ultrasound and photoacoustic are presented. The results are then analyzed for understanding the pigment map and its relation to salmon behavior to external stimuli. The photoacoustic images are compared with the optical images and analyzed further. A custom colormap and alpha map is designed and the matrices responsible for PAM and ultrasound are inserted together to overlay the ultrasound image and PAM image on top of each other. CONCLUSIONS: In this study, we propose an approach that combines scanning acoustic microscopy (SAM) images with PAM images for providing a comprehensive understanding of the salmon skin tissue. Overlaying acoustic and photoacoustic images enabled unique visualization of tissue morphology, with respect to identification of structural features in the context of their pigment distribution.

Detecting and analyzing nanoscale motion patterns of vesicles, smaller than the microscope resolution (~250 nm), inside living biological cells is a challenging problem. State-of-the-art CV approaches based on detection, tracking, optical flow or deep learning perform poorly for this problem. We propose an integrative approach, built upon physics based simulations, nanoscopy algorithms, and shallow residual attention network to make it possible for the first time to analysis sub-resolution motion patterns in vesicles that may also be of sub-resolution diameter. Our results show state-of-the-art performance, 89% validation accuracy on simulated dataset and 82% testing accuracy on an experimental dataset of living heart muscle cells imaged under three different pathological conditions. We demonstrate automated analysis of the motion states and changed in them for over 9000 vesicles. Such analysis will enable large scale biological studies of vesicle transport and interaction in living cells in the future.