Xylem Vessels Segmentation Through a Deep Learning Approach: a First Look

Abstract

Xylem is a vascular tissue that conveys water and dissolved minerals from the roots to the rest of the plant and also provides physical support. The most important cells present in xylem are called vessels. These cells are arranged to form long pipes that carry water through the tree. The identification, counting and subsequent characterization of xylem vessels is essential for monitoring tree health and its relationship with climatic conditions. Although automatic and semi-automatic image processing tools are available to analyze the structure of xylem at the cellular level, they usually require the supervision of an expert to obtain optimal segmentation, making it a highly time-consuming process. To overcome this limitation, a Convolutional Neural Network model was used to process digital images of 23 branch sections in order to segment the xylem vessels. The obtained results were compared with other two classical methods, Otsu's thresholding method, and an active contour method known as Chan-Vese segmentation algorithm. The obtained results show the potential of convolutional neural networks to overcome aspects such as non-homogeneous illumination of images, where conventional methods tend to obtain unsatisfactory results.