A Semi-Supervised Model for Non-Cellular Elements Segmentation in Microscopy Images of Wood

In wood science, accurate segmentation of non-cellular elements in microscopy images is critical for assessing wood quality and understanding growth patterns. Yet, it is challenging due to the complex morphology of wood components. This work explores the development of a semi-supervised deep learning model for segmenting non-cellular elements in wood microscopy images of Norway spruce, an essential source for construction materials in Europe, addressing the challenge of manual annotation’s labor intensity and expertise requirement. The segmentation model employs advanced deep learning architectures, including Convolutional Neural Networks and a Vision Transformer, to capture the intrinsic patterns embedded in wood structures. We proposed a Pixel-level Guided Mean-Teacher (PG-MT) framework as an improvement to the Mean-Teacher semi-supervised learning technique. Our framework enables pixel-level guided correction to enhance segmentation accuracy and model robustness with limited labeled datasets. Our experimental evaluations show that the proposed PG-MT framework improved the Dice score for medullary ray segmentation by 0.95% and the IoU score by 1.14% over the Uncertainty-Aware Mean-Teacher (UA-MT) framework. Additionally, the integration with laboratory instruments emphasizes the model’s effectiveness in accurately estimating cross-sectional cell wall thickness, demonstrating a strong correlation with X-ray measurements. This result validates the model’s practical applicability in laboratory settings, enhancing the analysis of wood properties. This work provides a robust semi-supervised DL framework for segmenting non-cellular elements in wood microscopy images, significantly reducing the annotation burden and paving the way for more automated and precise wood property analysis.