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Philips, W.

Publications and source records attributed to Philips, W..

2 recordsLinked to original sources

Large scale in vivo acquisition, segmentation and 3D reconstruction of cortical vasculature using functional ultrasound imaging.

The brain is composed of a dense and ramified vascular network comprising various sizes of arteries, veins, and capillaries. One way to assess the risk of cerebrovascular pathologies is to use computational models to predict the physiological effects of a reduction of blood supply and correlate these responses with observations of brain damage. Therefore, it is crucial to establish a detailed 3D organization of the brain vasculature, which could be used to develop more accurate in silico models. For this purpose, we have adapted our open-access functional ultrasound imaging platform previously designed for recording brain-wide activity that is now capable of fast and reproducible acquisition, segmentation, and reconstruction of the cortical vasculature. For the first time, it allows us to digitize the cortical vasculature in awake rodents with a [~]100 {micro}m3 spatial resolution. Contrary to most available strategies, our approach can be performed in vivo within minutes. Moreover, it is easy to implement since it neither requires exogenous contrast agents nor long post-processing time. Hence, we performed a cortex-wide reconstruction of the vasculature and its quantitative analysis, including i) classification of descending arteries versus ascending veins in more than 1500 vessels/animal, ii) quick estimation of their length. Importantly, we confirmed the relevance of our approach in a model of cortical stroke, which enables quick visualization of the ischemic lesion. This development contributes to extending the capabilities of ultrasound neuroimaging to understand better cerebrovascular pathologies such as stroke, vascular cognitive impairment, and brain tumors and is highly scalable for the clinic.

neuroscience↗

A ‘Human-in-the-Loop’ Approach for Semi-automated Image Restoration in Electron Microscopy

The recent advent of 3D in Electron Microscopy (EM) has allowed for detection of detailed sub-cellular nanometer resolution structures. While being a scientific breakthrough, this has also caused an explosion in dataset size, necessitating the development of automated workflows. Automated workflows typically benefit reproducibility and throughput compared to manual analysis. The risk of automation is that it ignores the expertise of the microscopy user that comes with manual analysis. To mitigate this risk, this paper presents a hybrid paradigm. We propose a human-in-the-loop (HITL) approach that combines expert microscopy knowledge with the power of large-scale parallel computing to improve EM image quality through advanced image restoration algorithms. An interactive graphical user interface, publicly available as an ImageJ plugin, was developed to allow biologists to use our framework in an intuitive and user-friendly fashion. We show that this plugin improves visualization of EM ultrastructure and subsequent (semi-)automated segmentation and image analysis.

bioinformatics↗