Abstract
Automatic glia reconstruction is essential for the dynamic analysis of microglia motility and morphology, notably so in research on neurodegenerative diseases. In this paper, we propose an automatic 3D tracing algorithm called C3VFC that uses vector field convolution to find the critical points along the centerline of an object and trace paths that traverse back to the soma of every cell in an image. The solution provides detection and labeling of multiple cells in an image over time, leading to multi-object reconstruction. The reconstruction results can be used to extract bioinformatics from temporal data in different settings. The C3VFC reconstruction results found up to a 53% improvement on the next best performing state-of-the-art tracing method. C3VFC achieved the highest accuracy scores, in relation to the baseline results, in four of the five different measures: Entire structure average, the average bi-directional entire structure average, the different structure average, and the percentage of different structures.
Highlights
The recent realization that the central nervous system (CNS) and the immune system are not two isolated systems, but rather connected systems, implies the relationship and interaction between neurons and immune cells in the CNS [1,2]
We compare our results with fast marching minimum spanning tree (FMST) and minimum spanning tree (MST) since both algorithms achieve higher or similar accuracy reconstruction scores than results from all path pruning version 2 (APP2) in numerous studies
The entire multi-cellular image was input into C3VFC and the output was the reconstruction image and SWC file format for all detected cells that were labeled over consecutive time frames
Summary
The recent realization that the central nervous system (CNS) and the immune system are not two isolated systems, but rather connected systems, implies the relationship and interaction between neurons and immune cells in the CNS [1,2]. The convolution-based approach was used to reconstruct a skeleton for neuron images in [33] by acquiring a VFC medialness map, an enhanced medial-axis image, which they used to create a graph minimum spanning tree. These methods rely on finding the correct scale to accurately reconstruct the image, but result in disjointed segments due to intensity inhomogeneity and noisy images. Attempting to reconnect disjointed segments in glia images based on orientation and distance result in incorrect connections due to the complexity of branches within one cell and between other intra-cell branches
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