发表机构
University of Oviedo; Institute of Biotechnology of Asturias (IUBA)（奥维耶多大学; 阿斯图里亚斯生物技术研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究开发了一款名为NeuroAdaptTrainer的Fiji/ImageJ插件，整合YOLO实例分割模型，支持神经元自动检测、交互式校正及迁移学习，降低非专业用户使用深度学习分割技术的门槛。
AI 中文摘要
神经元培养显微图像中的神经元计数与分割是神经科学研究中一项常规且耗时的任务，传统上通过人工检查或半自动工具完成。我们提出NeuroAdaptTrainer，这是一款开源的Fiji/ImageJ插件，将YOLO实例分割模型直接整合到显微研究者的工作流程中。该插件允许用户对单张或批量图像运行自动神经元检测，在Fiji内手动校正检测结果，并利用这些校正结果通过迁移学习使模型适应新的成像条件。内置的外部验证模块可对基础模型与适应后的模型在预留标注集上进行定量比较。NeuroAdaptTrainer降低了非专业用户利用深度学习分割技术的门槛，同时将专家监督置于工作流程的核心。
英文摘要
Neuron counting and segmentation in microscopy images of neuronal cultures is a routine and time-consuming task in neuroscience research, traditionally performed through manual inspection or semi-automatic tools. We present NeuroAdaptTrainer, an open-source Fiji/ImageJ plugin that integrates a YOLO instance-segmentation model directly into the microscopist's workflow. The plugin allows a user to run automatic neuron detection on a single image or a batch of images, manually correct the resulting detections from within Fiji, and use those corrections to adapt the model to new imaging conditions via transfer learning. A built-in external validation module allows the base and adapted models to be compared quantitatively on a held-out annotated set. NeuroAdaptTrainer lowers the barrier for non-specialist users to benefit from deep-learning-based segmentation while keeping expert supervision at the center of the workflow.
