Photoacoustic imaging is an emerging biomedical imaging technique that combines optical contrast and ultrasound resolution to create unprecedented light absorption contrast in deep tissue. Thanks to its fusional imaging advantages, photoacoustic imaging can provide multiple structural and functional insights into biological tissues such as blood vasculatures and tumors and monitor the kinetic movements of hemoglobin and lipids. To better visualize and analyze the regions of interest, segmentation and quantitative analyses were used to extract several biological factors, such as the intensity level changes, diameter, and tortuosity of the tissues. Over the past 10 years, classical segmentation methods and advances in deep learning approaches have been utilized in research investigations. In this review, we provide a comprehensive review of segmentation and quantitative methods that have been developed to process photoacoustic imaging in preclinical and clinical experiments. We focus on the parametric reliability of quantitative analysis for semantic and instance-level segmentation. We also introduce the similarities and alternatives of deep learning models in qualitative measurements using classical segmentation methods for photoacoustic imaging.

Mosaic imaging is a computer vision process that is used for merging multiple overlapping imaging patches into a wide-field-of-view image. To achieve a wide-field-of-view photoacoustic microscopy (PAM) image, the limitations of the scan range of PAM require a merging process, such as marking the location of patches or merging overlapping areas between adjacent images. By using the mosaic imaging process, PAM shows a larger field view of targets and preserves the quality of the spatial resolution. As an essential process in mosaic imaging, various feature generation methods have been used to estimate pairs of image locations. In this study, various feature generation algorithms were applied and analyzed using a high-resolution mouse ear PAM image dataset to achieve and optimize a mosaic imaging process for wide-field PAM imaging. We compared the performance of traditional and deep learning feature generation algorithms by estimating the processing time, the number of matches, good matching ratio, and matching efficiency. The analytic results indicate the successful implementation of wide-field PAM images, realized by applying suitable methods to the mosaic PAM imaging process.

SPIE Digital Library Proceedings

Photoacoustic microscopy (PAM) enables high‐resolution, label‐free imaging by detecting ultrasound generated from pulsed‐laser absorption. For high‐fidelity spatiospectral imaging with single‐raster scanning, two spectrally distinct pulses should arrive nearly simultaneously to preserve spatial–temporal correlation, and at least one wavelength should be tunable for exogenous agents. We present spatiospectral PAM (SS‐PAM) driven by a gain‐switched Ti:sapphire laser engineered for this purpose. The source provides 735–855 nm tunability spanning hemoglobin and NIR‐I agents (e.g., indocyanine green, ICG) and is intrinsically synchronized to a 532 nm pump with a ≈150 ns gain‐switch delay, producing dual pulses for spatiospectral excitation. Operating at 20–30 kHz with pulse energies up to 63 µJ, SS‐PAM yields temporally correlated, high‐contrast excitation beyond sequential or dual‐laser schemes. In vivo, we quantified hemoglobin oxygen saturation (sO ₂ ) under controlled hypoxia, tracked vascular transport after intravenous ICG, visualized peritumoral lymphatic drainage, and analyzed oxygen extraction in tumor‐associated microvasculature, establishing SS‐PAM as a robust platform for synchronized, vessel‐level functional and molecular imaging.

SPIE Digital Library Proceedings