Automated Morphological Analysis of Neurons in Fluorescence Microscopy Using YOLOv8

TL;DR

This paper introduces an automated pipeline using YOLOv8 for high-accuracy neuron segmentation and morphological analysis in fluorescence microscopy images, significantly reducing manual effort in neuroscience research.

Contribution

The study presents a novel automated framework combining YOLOv8 with morphological feature extraction, achieving over 97% segmentation accuracy and 75.32% measurement accuracy.

Findings

Segmentation accuracy exceeds 97%

Morphological measurement accuracy reaches 75.32%

Framework reduces manual annotation effort

Abstract

Accurate segmentation and precise morphological analysis of neuronal cells in fluorescence microscopy images are crucial steps in neuroscience and biomedical imaging applications. However, this process is labor-intensive and time-consuming, requiring significant manual effort and expertise to ensure reliable outcomes. This work presents a pipeline for neuron instance segmentation and measurement based on a high-resolution dataset of stem-cell-derived neurons. The proposed method uses YOLOv8, trained on manually annotated microscopy images. The model achieved high segmentation accuracy, exceeding 97%. In addition, the pipeline utilized both ground truth and predicted masks to extract biologically significant features, including cell length, width, area, and grayscale intensity values. The overall accuracy of the extracted morphological measurements reached 75.32%, further supporting the effectiveness of the proposed approach. This integrated framework offers a valuable tool for automated analysis in cell imaging and neuroscience research, reducing the need for manual annotation and enabling scalable, precise quantification of neuron morphology.