🤖 AI Summary
This work addresses the inefficiency and reliance on manual labor in neuronal segmentation and counting within neuroscience research by developing a Fiji/ImageJ plugin that integrates a YOLO-based instance segmentation model. The tool enables fully automated neuron detection, supports interactive manual correction, and incorporates online transfer learning using user-provided annotations to continuously refine the model. It also includes a built-in validation module for quantitative performance evaluation. By seamlessly embedding deep learning into established microscopy image analysis workflows, this approach significantly lowers the barrier for non-expert users to leverage state-of-the-art segmentation models while preserving expert oversight, thereby enhancing both segmentation efficiency and model adaptability.
📝 Abstract
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.