Segmentation and Characterization of Macerated Fibers and Vessels Using
  Deep Learning
Saqib Qamar Abu Imran Baba Stéphane Verger Magnus Andersson
Purpose: Wood comprises different cell types, such as fibers and vessels,
defining its properties. Studying their shape, size, and arrangement in
microscopic images is crucial for understanding wood samples. 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. Results: In this work, we develop an automatic deep
learning segmentation approach that utilizes the one-stage YOLOv8 model for
fast and accurate fiber and vessel segmentation and characterization in
microscopy images. The model can analyze 32640 x 25920 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. Conclusion: 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.