CORAL: A Benchmark for Structure-aware and Brain-wide Neuron Reconstruction in Light Microscopy

2026-08-31

AI Summary

本文提出了CORAL基准，用于评估从光镜图像中自动重建神经元的方法，旨在解决结构准确性及全脑尺度下的可扩展性问题。

Abstract

Automatic neuron reconstruction from light microscopy images is a central problem in computational neuroanatomy. While recent methods have achieved encouraging results on local image blocks, it remains unclear whether such progress translates to reconstruction that is both structurally accurate and scalable to the whole-brain scale. We present CORAL, the first benchmark for structure-aware evaluation of automatic neuron reconstruction from light microscopy images at both local and whole-brain scales. Built on a high-quality whole-brain fMOST dataset with carefully curated annotations, CORAL establishes two progressive tasks: block-level reconstruction, which evaluates reconstruction methods under limited spatial context, and brain-wide reconstruction, which assesses complete neuron reconstruction at the whole-brain scale. To account for topological correctness beyond geometric distance similarity, we introduce a structure-aware metric based on fiber prediction. To further achieve complete neuron reconstruction across the entire brain, we develop a brain-wide neuron tracing framework that extends arbitrary local reconstruction methods to the whole-brain scale through an iterative local-to-global process. Using this benchmark, we provide the first structure-aware comparison of mainstream methods for local neuron reconstruction and further evaluate their performance in brain-wide reconstruction. Our results underscore the importance of structure-aware evaluation and the need for more robust methods for complete neuron reconstruction.

Problem

• neuron reconstruction
• light microscopy
• whole-brain scale
• structure-aware

Innovation

• structure-aware evaluation
• whole-brain scale
• fiber prediction
• brain-wide neuron tracing framework