🤖 AI Summary
This study addresses the skeletonization measurement biases caused by the fragility and erroneous merging of thin fluorescently labeled cellular branches. We propose a graph-based workflow integrating a deterministic morphology engine with LLM-assisted optimization, achieving high-fidelity structural reconstruction through multi-scale evidence extraction, hysteresis segmentation, constrained skeleton refinement, and graph morphology. Furthermore, this work pioneers an auditable natural language interaction paradigm grounded in computational determinism. Evaluated across three datasets, the proposed method achieves state-of-the-art Skeleton F1 and clDice scores alongside minimal length errors, while attaining a 94.0% success rate on natural language tasks. Ultimately, this framework effectively balances fine-structure preservation with flexible human-machine interaction.
📝 Abstract
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.