Biology subjects

Lefebvre, T. L.

Publications and source records attributed to Lefebvre, T. L..

3 recordsLinked to original sources

bioRxiv2023

Segmentation of 3D blood vessel networks using unsupervised deep learning

Mesoscopic photoacoustic imaging (PAI) enables label-free visualisation of vascular networks in tissue at high contrast and resolution. The segmentation of vascular networks from 3D PAI data and interpretation of their meaning in the context of physiological and pathological processes is a crucial but time consuming and error-prone task. Deep learning holds potential to solve these problems, but current supervised analysis frameworks require human-annotated ground-truth labels. Here, we overcome the need for ground-truth labels by introducing an unsupervised image-to-image translation deep learning model called 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 physics of the PAI system in order to learn how to segment vasculature from 3D biomedical images. With a variety of in silico, in vitro and in vivo data, including patient-derived breast cancer xenograft models, we show that VAN-GAN facilitates accurate and unbiased segmentation of 3D vascular networks from PAI data volumes. By leveraging synthetic data to reduce the reliance on manual labelling, VAN-GAN lowers the barrier to entry for high-quality blood vessel segmentation to benefit users in the life sciences applying PAI to studies of vascular structure and function.

biophysics↗

bioRxiv2022

Performance evaluation of mesoscopic photoacoustic imaging

Photoacoustic mesoscopy visualises vascular architecture and associated tissue structures at high resolution at up to 3 mm depth. The technique has shown promise in both preclinical and clinical imaging studies, with possible applications in oncology and dermatology, however, the accuracy and precision of photoacoustic mesoscopy has not been well established. Here, we present a performance evaluation of a commercial photoacoustic mesoscopy system for imaging vascular structures. Typical artefact types are first highlighted and limitations due to non-isotropic illumination and detection are evaluated with respect to rotation, angularity, and depth of the target. Then, using tailored phantoms and mouse models we demonstrate high system precision, with acceptable coefficients of variation (COV) between repeated scans (short term (1h): COV=1.2%; long term (25 days): COV=9.6%), from target repositioning (without: COV=1.2%, with: COV=4.1%), or from varying in vivo user experience (experienced: COV=15.9%, unexperienced: COV=20.2%). While our findings support the robustness of the technique, they also underscore the general challenges of limited field-of-view photoacoustic systems in accurately imaging vessel-like structures, thereby guiding users to correctly interpret biologically-relevant information.

bioengineering↗

bioRxiv2021

Quantification of vascular networks in photoacoustic mesoscopy

Mesoscopic photoacoustic imaging (PAI) enables non-invasive visualisation of tumour vasculature and has the potential to assess prognosis and therapeutic response. Currently, evaluating vasculature using mesoscopic PAI involves visual or semi-quantitative 2D measurements, which fail to capture 3D vessel network complexity, and lack robust ground truths for assessment of segmentation accuracy. Here, we developed an in silico, phantom, in vivo, and ex vivo-validated end-to-end framework to quantify 3D vascular networks captured using mesoscopic PAI. We applied our framework to evaluate the capacity of rule-based and machine learning-based segmentation methods, with or without vesselness image filtering, to preserve blood volume and network structure by employing topological data analysis. We first assessed segmentation performance against ground truth data of in silico synthetic vasculatures and a photoacoustic string phantom. Our results indicate that learning-based segmentation best preserves vessel diameter and blood volume at depth, while rule-based segmentation with vesselness image filtering accurately preserved network structure in superficial vessels. Next, we applied our framework to breast cancer patient-derived xenografts (PDXs), with corresponding ex vivo immunohistochemistry. We demonstrated that the above segmentation methods can reliably delineate the vasculature of 2 breast PDX models from mesoscopic PA images. Our results underscore the importance of evaluating the choice of segmentation method when applying mesoscopic PAI as a tool to evaluate vascular networks in vivo.

bioengineering↗