Unsupervised Segmentation of 3D Microvascular Photoacoustic Images Using Deep Generative Learning.

ABSTRACT: Mesoscopic photoacoustic imaging (PAI) enables label-free visualization of vascular networks in tissues with high contrast and resolution. Segmenting these networks from 3D PAI data and interpreting their physiological and pathological significance is crucial yet challenging due to the time-consuming and error-prone nature of current methods. Deep learning offers a potential solution; however, supervised analysis frameworks typically require human-annotated ground-truth labels. To address this, an unsupervised image-to-image translation deep learning model is introduced, the Vessel Segmentation Generative Adversarial Network (VAN-GAN). VAN-GAN integrates synthetic blood vessel networks that closely resemble real-life anatomy into its training process and learns to replicate the underlying

SUBMITTER: Sweeney PW 

PROVIDER: S-EPMC11348209 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature