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.

Motivation: A fine digital tracing of complete neural morphology from a whole brain imaging dataset is highly required in the neuroscience field. With the participation of human experts in the reconstruction workflow, the complicated annotation challenge brought by the whole brain scale could be addressed. Yet different degrees of deviation at branches and bifurcation point are produced, which hurts the utility of the reconstruction data in downstream analysis. Results: We propose a neuron reconstruction refinement strategy (NRRS) to fill the gap mentioned ahead. Our strategy partitions neuron morphology reconstruction into fixed-size segments and resolves the deviation problems by two steps of neuron tracing. Furthermore, we build a synthetic dataset severed as validation data for quantifying the exact performance. We show that NRRS can handle most of the defined deviation errors while existing solutions hardly achieve matched performance. In applying our method to SEU-Allen dataset containing nearly 2000 complete neuron reconstructions, we present large improvements obtained in the accuracy of the neuron skeleton representation, the task of radius estimation and axonal bouton detection. The results demonstrate the indispensability of our strategy in refining neuron morphology reconstruction.