Analytical Approaches for the Segmentation of the Zebrafish Brain Vasculature

With advancements in imaging techniques, data visualization allows new insights into fundamental biological processes of development and disease. However, although biomedical science is heavily reliant on imaging data, interpretation of datasets is still often based on subjective visual assessment rather than rigorous quantitation. This overview presents steps to validate image processing and segmentation using the zebrafish brain vasculature data acquired with light sheet fluorescence microscopy as a use case.

Blood vessels are of particular interest to both medical and biomedical science. Specific image enhancement filters have been developed that enhance blood vessels in imaging data prior to segmentation. Using the Sato enhancement filter as an example, we discuss how filter application can be evaluated and optimized. Approaches from the medical field such as simulated, experimental, and augmented datasets can be used to gain the most out of the data at hand. Using such datasets, we provide an overview of how biologists and data analysts can assess the accuracy, sensitivity, and robustness of their segmentation approaches that allow extraction of objects from images. Importantly, even after optimization and testing of a segmentation workflow (e.g., from a particular reporter line to another or between immunostaining processes), its generalizability is often limited, and this can be tested using double‐transgenic reporter lines. Lastly, due to the increasing importance of deep learning networks, a comparative approach can be adopted to study their applicability to biological datasets.

In summary, we present a broad methodological overview ranging from image enhancement to segmentation with a mixed approach of experimental, simulated, and augmented datasets to assess and validate vascular segmentation using the zebrafish brain vasculature as an example. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

• Simulated, experimental, and augmented datasets provide an alternative to overcome the lack of segmentation gold standards and phantom models for zebrafish cerebrovascular segmentation.

• Direct generalization of a segmentation approach to the data for which it was not optimized (e.g., different transgenics or antibody stainings) should be treated with caution.

• Comparison of different deep learning segmentation methods can be used to assess their applicability to data. Here, we show that the zebrafish cerebral vasculature can be segmented with U‐Net–based architectures, which outperform SegNet architectures.