用YOLO模型自动分割神经元，支持交互修正与迁移学习。
NeuroAdaptTrainer: A Fiji/ImageJ Plugin for YOLO-Based Neuron Segmentation, InteractiveCorrection and Transfer Learning
- 集成YOLO实例分割模型，直接嵌入Fiji工作流
- 支持单图/批量处理，修正后可迁移学习适配新条件
- 内置验证模块，量化对比基线与优化模型性能
在神经元培养的显微图像中进行神经元计数与分割是神经科学研究中的常规但耗时的任务，传统上依赖人工或半自动工具完成。我们提出NeuroAdaptTrainer，一个开源的Fiji/ImageJ插件，将YOLO实例分割模型直接整合进微观学家的工作流程。该插件支持单张或批量图像的自动神经元检测，用户可在Fiji内手动修正结果，并利用这些修正数据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在保留标注集上定量比较基础模型与优化后模型的表现。NeuroAdaptTrainer降低了非专业用户使用深度学习分割的门槛，同时保持专家监督在流程中的核心地位。
原文摘要 · Abstract (English)
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.
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