Abstract
Automated analysis of multi-dimensional microscopy images has become an integral part of modern research in life science. Most available algorithms that provide sufficient segmentation quality, however, are infeasible for a large amount of data due to their high complexity. In this contribution we present a fast parallelized segmentation method that is especially suited for the extraction of stained nuclei from microscopy images, e.g., of developing zebrafish embryos. The idea is to transform the input image based on gradient and normal directions in the proximity of detected seed points such that it can be handled by straightforward global thresholding like Otsu’s method. We evaluate the quality of the obtained segmentation results on a set of real and simulated benchmark images in 2D and 3D and show the algorithm’s superior performance compared to other state-of-the-art algorithms. We achieve an up to ten-fold decrease in processing times, allowing us to process large data sets while still providing reasonable segmentation results.
Highlights
Recent developments of fluorescence microscopy techniques have revealed unprecedented possibilities for the in vivo analysis of developing specimens [1,2]
Our proposed method seemed to be positioned on a good average position in the quality comparison and yielded the best values for Hausdorff Metric (HM) and Normalized Sum of Distances (NSD)
It was shown that the proposed algorithm performed up to ten times faster than other established methods while still providing sufficient segmentation quality for subsequent analysis steps
Summary
Recent developments of fluorescence microscopy techniques have revealed unprecedented possibilities for the in vivo analysis of developing specimens [1,2]. The tremendous amount of acquired 3D+t spatio-temporal image data, cannot reasonably be analyzed manually. Highly automated procedures for the analysis of such biological image data have become an increasingly important component of current research in the life sciences [5,6,7]. For example in typical experiments, imaging the development of a zebrafish embryo within the first ten hours post fertilization (hpf) results in several thousand of 3D image stacks with file sizes of multiple Gigabytes per image stack [8,9]. Even a modest experiment with a single embryo accumulates multiple Terabytes of raw data
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