NRRS: a re-tracing strategy to refine neuron reconstruction

Abstract

It is crucial to develop accurate and reliable algorithms for fine reconstruction of neural morphology from whole-brain image datasets. Even though the involvement of human experts in the reconstruction process can help to ensure the quality and accuracy of the reconstructions, automated refinement algorithms are necessary to handle substantial deviations problems of reconstructed branches and bifurcation points from the large-scale and high-dimensional nature of the image data. Our proposed Neuron Reconstruction Refinement Strategy (NRRS) is a novel approach to address the problem of deviation errors in neuron morphology reconstruction. Our method partitions the reconstruction into fixed-size segments and resolves the deviation problems by re-tracing in two steps. We also validate the performance of our method using a synthetic dataset. Our results show that NRRS outperforms existing solutions and can handle most deviation errors. We apply our method to SEU-ALLEN/BICCN dataset containing 1741 complete neuron reconstructions and achieve remarkable improvements in the accuracy of the neuron skeleton representation, the task of radius estimation and axonal bouton detection. Our findings demonstrate the critical role of NRRS in refining neuron morphology reconstruction. Availability and implementation The proposed refinement method is implemented as a Vaa3D plugin and the source code are available under the repository of vaa3d_tools/hackathon/Levy/refinement. The original fMOST images of mouse brains can be found at the BICCN’s Brain Image Library (BIL) (https://www.brainimagelibrary.org). The synthetic dataset is hosted on GitHub (https://github.com/Vaa3D/vaa3d_tools/tree/master/hackathon/Levy/refinement). Supplementary information Supplementary data are available at Bioinformatics Advances online.

Full-Spectrum Neuronal Diversity and Stereotypy through Whole Brain Morphometry

“…While the neuron reconstructions were manually edited by multiple annotators to ensure the correctness of branching patterns, the limited precision of spatial (3-D) pinpointing in manual annotation caused the skeleton of almost every neuron to deviate slightly from the center of the image signal of the skeleton. Therefore, we developed an automatic approach to correct such aberration (Methods) (Li et al, 2023), and generated precisely centered neuron skeletons. This development was also leveraged for the subsequent analyses of axonal varicosities.…”

“…By utilizing the complete axons in SEU-A1891 neurons, we identified both types of boutons. To maximize accuracy, we refined the manually annotated skeleton of neurons using an automated skeleton de-skewing algorithm (Li et al, 2023), followed by approximating boutons using a Gaussian distribution model (Methods; Supplementary Figure S8). We identified 2.58 million axonal boutons in total for SEU-A1891, or 1,363 boutons per neuron.…”