MorphoBranch：用于分支细胞结构形态计量分析的精细结构保持工作台
MorphoBranch: A Fine-Structure-Preserving Workbench for Morphometric Analysis of Branched Cellular Structures
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中文总结 AI 辅助
MorphoBranch提出结合确定性形态计量引擎与LLM辅助细化引擎的工作台，在神经元轴突和小胶质细胞图像上实现精细结构保持的形态计量分析，取得最优性能与94%任务成功率。
中文摘要 AI 辅助
背景与目标：荧光标记的细胞树状结构提供了神经元和小胶质细胞形态的读数，但精细且弱标记的过程容易发生碎片化和虚假连接，从而偏倚基于骨架的测量。我们提出了MorphoBranch，一个保持精细结构、可人工审查的形态计量分析工作台，用于分支细胞结构。方法：MorphoBranch结合了确定性的形态计量引擎和LLM辅助的细化引擎。形态计量引擎实现了图像到图的工作流程，整合了多尺度结构证据提取、滞后分割、证据约束的骨架细化和基于图的形态计量。细化引擎将自然语言请求映射到注册的动作，用于参数调整、预览执行、指标报告和不支持请求的处理，而图像处理和定量计算保持确定性和可审查性。结果：MorphoBranch在两个公共神经元轴突数据集AxonMIP和AxonStack以及内部的小胶质细胞荧光图像数据集CellMorph上进行了评估。在所有三个数据集上，它在评估的方法中实现了最高的骨架F1和clDice，以及最低的长度估计误差，同时在AxonMIP和AxonStack上实现了最高的Dice和IoU。在150个自然语言任务中，细化引擎实现了94.0%的端到端成功率。结论：这些结果表明，MorphoBranch为分支细胞结构的形态计量分析提供了可重复、可人工审查的工作流程。它支持在神经元轴突和小胶质细胞荧光图像上保持精细结构的量化，同时保持可检查和可重复的分析工作流程。
英文摘要
Background and Objectives: Fluorescence-labeled cellular arbors provide readouts of neuronal and microglial morphology, but fine and weakly labeled processes are prone to fragmentation and false connections that bias skeleton-based measurements. We present MorphoBranch, a fine-structure-preserving, human-reviewable workbench for morphometry of branched cellular structures. Methods: MorphoBranch combines a deterministic Morphometry Engine with an LLM-assisted Refinement Engine. The Mor- phometry Engine implements an image-to-graph workflow integrating multiscale structural evidence extraction, hysteresis segmen- tation, evidence-constrained skeleton refinement, and graph-based morphometry. The Refinement Engine maps natural-language requests to registered actions for parameter adjustment, preview execution, metric reporting, and unsupported-request handling, while image processing and quantitative computation remain deterministic and reviewable. Results: MorphoBranch was evaluated on two public neuronal axon datasets, AxonMIP and AxonStack, and the in-house Cell- Morph dataset of microglial fluorescence images. It achieved the highest Skeleton F1 and clDice and the lowest length-estimation error among the evaluated methods on all three datasets, while also achieving the highest Dice and IoU on AxonMIP and Axon- Stack. Across 150 natural-language tasks, the Refinement Engine achieved a 94.0% end-to-end success rate. Conclusions: These results demonstrate that MorphoBranch provides a reproducible, human-reviewable workflow for mor- phometric analysis of branched cellular structures. It supports fine-structure-preserving quantification across neuronal axon and microglial fluorescence images while maintaining inspectable and reproducible analysis workflows.
发表机构
- Zhejiang University of Finance and Economics（浙江财经大学）
- Zhejiang University（浙江大学）
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