发表机构
Tsinghua University; Tsinghua Laboratory of Brain and Intelligence; IDG/McGovern Institute for Brain Research; School of Basic Medical Sciences; Tsinghua-Peking Center for Life Sciences（清华大学; 清华大学生物医学工程系脑与智能实验室; IDG/麦戈文脑科学研究所; 医学科学系; 清华-北京大学生命科学联合中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出首个结构感知的神经元重建基准CORAL，构建相关任务与指标，开发全脑追踪框架，对比主流方法并凸显结构感知评估的必要性。
AI 中文摘要
从光显微图像自动重建神经元是计算神经解剖学的核心问题。尽管近期方法在局部图像块上取得了令人鼓舞的结果，但这类进展能否转化为同时具备结构准确性且可扩展至全脑规模的重建结果仍不明确。我们提出CORAL，这是首个针对光显微图像自动神经元重建的结构感知评估基准，覆盖局部与全脑两个规模。CORAL基于高质量的全脑fMOST数据集及精心整理的标注构建，设立两项递进任务：块级重建，评估空间上下文有限情况下的重建方法；全脑重建，评估全脑范围内的完整神经元重建。为考量几何距离相似性之外的拓扑正确性，我们引入基于纤维预测的结构感知指标。为进一步实现全脑范围内的完整神经元重建，我们开发了全脑神经元追踪框架，通过迭代的局部到全局过程将任意局部重建方法扩展至全脑规模。利用该基准，我们首次对主流局部神经元重建方法开展结构感知比较，并进一步评估其全脑重建性能。我们的结果强调了结构感知评估的重要性，以及对更鲁棒的完整神经元重建方法的需求。
英文摘要
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.
