In-vivo 3D imaging of Zebrafish's intersegmental vessel development by a bi-directional light-sheet illumination microscope

Efficient and Accurate Semi-Automatic Neuron Tracing with Extended Reality

Neuron tracing, alternately referred to as neuron reconstruction, is the procedure for extracting the digital representation of the three-dimensional neuronal morphology from stacks of microscopic images. Achieving accurate neuron tracing is critical for profiling the neuroanatomical structure at single-cell level and analyzing the neuronal circuits and projections at whole-brain scale. However, the process often demands substantial human involvement and represents a nontrivial task. Conventional solutions towards neuron tracing often contend with challenges such as non-intuitive user interactions, suboptimal data generation throughput, and ambiguous visualization. In this paper, we introduce a novel method that leverages the power of extended reality (XR) for intuitive and progressive semi-automatic neuron tracing in real time. In our method, we have defined a set of interactors for controllable and efficient interactions for neuron tracing in an immersive environment. We have also developed a GPU-accelerated automatic tracing algorithm that can generate updated neuron reconstruction in real time. In addition, we have built a visualizer for fast and improved visual experience, particularly when working with both volumetric images and 3D objects. Our method has been successfully implemented with one virtual reality (VR) headset and one augmented reality (AR) headset with satisfying results achieved. We also conducted two user studies and proved the effectiveness of the interactors and the efficiency of our method in comparison with other approaches for neuron tracing.

Three-dimensional cerebral vasculature topological parameter extraction of transgenic zebrafish embryos with a filling-enhancement deep learning network

Quantitative analysis of zebrafish cerebral vasculature is essential for the study of vascular development and disease. We developed a method to accurately extract the cerebral vasculature topological parameters of transgenic zebrafish embryos. The intermittent and hollow vascular structures of transgenic zebrafish embryos, obtained from 3D light-sheet imaging, were transformed into continuous solid structures with a filling-enhancement deep learning network. The enhancement enables the extraction of 8 vascular topological parameters accurately. Quantitation of the zebrafish cerebral vasculature vessels with the topological parameters show a developmental pattern transition from 2.5 to 5.5 dpf.

Optimized U-Net model for 3D light-sheet image segmentation of zebrafish trunk vessels

The growth of zebrafish's vessels can be used as an indicator of the vascular development process and to study the biological mechanisms. The three-dimensional (3D) structures of zebrafish's trunk vessels could be imaged by state-of-art light-sheet fluorescent microscopy with high efficiency. A large amount of data was then produced. Accurate segmentation of these 3D images becomes a new bottleneck for automatic and quantitative analysis. Here, we propose a Multi-scale 3D U-Net model to perform the segmentation of trunk vessels. The segmentation accuracies of 82.3% and 83.0%, as evaluated by the IoU (Intersection over Union) parameter, were achieved for intersegmental vessels and the dorsal longitudinal anastomotic vessels respectively. The growth of zebrafish vasculature from 42-62 hours was then analyzed quantitatively.

Airy-like beam-based light-sheet microscopy with improved FOV for zebrafish intracerebral hemorrhage

Airy light-sheet microscopy is rapidly gaining importance for imaging intact biological specimens because of the rapid speed, high resolution, and wide field nature of the imaging method. However, the depth of field (DOF) of the detection objective imposes limitations on the modulation transfer function (MTF) of the light sheet, which in turn affects the size of the field of view (FOV). Here we present an optimized phase modulation model, based on 'Airy-like' beam family, to stretch the curved lobes, which brings a wider FOV while maintaining high resolution. In addition, we further develop a planar 'Airy-like' light-sheet by two-photon excitation which can avoid the deconvolution process. We validated the new imaging method by performing a real-time monitoring of the dynamic process of cerebral hemorrhage in zebrafish larva. The proposed Airy-like beam-based light-sheet microscopy has great potential to be applied to the precise screening of cerebral hemorrhage-related drugs to help precision medicine in the future.