  Wood comprises different cell types, such as fibers, tracheids and vessels,
defining its properties. Studying cells' shape, size, and arrangement in
microscopy images is crucial for understanding wood characteristics. Typically,
this involves macerating (soaking) samples in a solution to separate cells,
then spreading them on slides for imaging with a microscope that covers a wide
area, capturing thousands of cells. However, these cells often cluster and
overlap in images, making the segmentation difficult and time-consuming using
standard image-processing methods. In this work, we developed an automatic deep
learning segmentation approach that utilizes the one-stage YOLOv8 model for
fast and accurate segmentation and characterization of macerated fiber and
vessel form aspen trees in microscopy images. The model can analyze 32,640 x
25,920 pixels images and demonstrate effective cell detection and segmentation,
achieving a mAP_{0.5-0.95} of 78 %. To assess the model's robustness, we
examined fibers from a genetically modified tree line known for longer fibers.
The outcomes were comparable to previous manual measurements. Additionally, we
created a user-friendly web application for image analysis and provided the
code for use on Google Colab. By leveraging YOLOv8's advances, this work
provides a deep learning solution to enable efficient quantification and
analysis of wood cells suitable for practical applications.
