Semantic Segmentation of CT Data of Oak Wood for the Creation of Wood Anatomical Climate Proxy Archives

In this study, an automated method was developed for the semantic segmentation of computed tomography ( CT ) data of oak wood linking the fields of wood anatomy and image analysis. The method reliably and rapidly segments whole vessels regardless of the presence of tylosis, which frequently is a major problem in wood anatomical studies. Analysis of the segmented vessels can contribute to the expansion of wood anatomical climate proxy archives. A machine learning approach was used for the automatic segmentation of the vessels, and a neural network was identified that performs the segmentation task with the smallest possible error. To achieve this, different U-Net models — widely used deep learning architectures — were trained and evaluated with manually masked and augmented CT training data. As a peak performance of this dataset, an exactness of 0.79 measured at the mean intersection over union (IoU) could be achieved. The U-Net could generalize new CT data of oak wood and show good results in predicting CT -like image acquisitions, such as a digital scan of a thin section. By using the U-Net with the CT data, it is possible to quantify vessel diameter at extraordinarily small spatial increments and additionally observe the vessels in 3D. The trained U-Net is also integrated into a user interface into which single or multiple CT images can be automatically read, predicted, post-processed and saved. The code and model file will be permanently archived on Zenodo and GitHub (Füting 2025) and is open for further development

Artificial neural network · Deep learning · Image segmentation · Segmentation · Advanced X-ray and CT Imaging · Advanced X-ray Imaging Techniques · Medical Imaging and Analysis

• U-Net

  Open Access•Olaf Ronneberger, Philipp Fischer et al.•Medical Image Computing and…•2015

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