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.

翻译：木材由纤维、管胞和导管等不同细胞类型构成，这些细胞决定了木材的特性。在显微图像中研究细胞的形态、尺寸与排列方式对于理解木材特征至关重要。通常，这需要将样本离析（浸泡）于溶液中以分离细胞，随后将其铺展于载玻片上，利用覆盖大视场的显微镜进行成像，从而捕获数千个细胞。然而，这些细胞在图像中常聚集与重叠，使得使用标准图像处理方法进行分割既困难又耗时。本研究开发了一种自动深度学习分割方法，利用单阶段YOLOv8模型对显微图像中离析的杨树纤维与导管进行快速、准确的分割与表征。该模型能够分析32,640 x 25,920像素的图像，并展现出有效的细胞检测与分割能力，实现了78%的mAP_{0.5-0.95}。为评估模型的鲁棒性，我们检测了来自已知具有较长纤维特征的转基因树系的纤维。所得结果与先前的手动测量具有可比性。此外，我们开发了一个用户友好的网络应用程序用于图像分析，并提供了可在Google Colab上使用的代码。通过利用YOLOv8的先进特性，本工作提供了一种深度学习解决方案，能够实现对木材细胞的高效量化与分析，适用于实际应用。