3D early embryogenesis image filtering by nonlinear partial differential equations.

Area of Science:

• Biomedical Imaging
• Computational Biology
• Image Processing

Background:

• Accurate analysis of 3D+time embryogenesis images is crucial for understanding cell lineage.
• Image filtering is a critical first step, requiring noise reduction, edge sharpening, and correction of acquisition errors.
• Existing methods face challenges in precisely identifying cell nuclei in complex biological datasets.

Purpose of the Study:

• To identify the optimal nonlinear diffusion filtering method and parameters for 3D laser scanning microscopy (LSM) images of zebrafish embryos.
• To evaluate filtering techniques for accurate cell nuclei detection and spatio-temporal lineage tree reconstruction.
• To compare the performance of regularized Perona-Malik, generalized mean curvature flow, tensor anisotropic diffusion, and Beltrami flow filters.

Main Methods:

• Application of nonlinear diffusion equations and numerical schemes, specifically finite volume and semi-implicit time discretization.
• Evaluation using Mean Hausdorff distance against a gold standard for isosurface accuracy.
• Comparison of connected components in regions of interest with known cell nuclei counts.

Main Results:

• The geodesic mean curvature flow (GMCF) filter demonstrated superior performance in edge preservation, noise reduction, and artifact correction.
• GMCF effectively separated artificially connected objects caused by LSM acquisition physics.
• Optimal filtering parameters were identified, enhancing the reliability of cell nuclei detection.

Conclusions:

• Nonlinear diffusion filtering, particularly GMCF, is a robust and reliable method for analyzing 3D LSM embryogenesis data.
• The developed numerical schemes are stable, fast, and parallelizable, suitable for large-scale image analysis.
• Optimized filtering significantly improves the accuracy of cell nuclei identification for constructing spatio-temporal cell lineage trees.