Evaluation of U-Net models.

Vascular bundles of bamboo are determinants for mechanical properties of bamboo material and for physiological properties of living bamboo. The morphology of vascular bundles reflecting mechanical and physiological functions differs not only within internode tissue but also among different internodes in the culm. Although the distribution of vascular bundle fibers has received much attention, quantitative evaluation of the morphological transformation of vascular bundles associated with spatial distribution patterns has been limited. In this study deep learning models were used to determine quantitative changes in the distribution and morphology of vascular bundles in the culms of moso bamboo (Phyllostachys pubescens). A precise model for extracting vascular bundles from cross-sectional images was constructed using the U-Net model. Analyses of extracted vascular bundles from different internodes showed significant changes in vascular bundle distribution and morphology among internodes. Vascular bundles in lower internodes showed outer relative position and larger area than those in upper internodes. Aspect ratio and eccentricity indicate that vascular bundles in internodes near the base have more elliptical morphology, with a long axis in the radial direction. The variational autoencoder model using extracted vascular bundles enabled simulation of the morphological transformation of vascular bundles along with radial direction. These deep learning models enabled highly accurate quantification of vascular bundle morphologies, and will contribute to a further understanding of bamboo development as well as evaluation of the mechanical and physiological properties of bamboo.</div

Preparation of training data for extracting vascular bundles from cross section images.

Different internodes (the 2nd, 12th, 22nd, and 32nd internodes) in culms (A) were used to prepare blocks and cross sectional images were obtained (B, left side), by which mask images were drawn by hand (B, right side). Scale = 1 mm. Original images and mask images pairs were cropped with gray scale (C) to train U-Net model.</p

A movie of figures created by the VAE model.

Vascular bundles of bamboo are determinants for mechanical properties of bamboo material and for physiological properties of living bamboo. The morphology of vascular bundles reflecting mechanical and physiological functions differs not only within internode tissue but also among different internodes in the culm. Although the distribution of vascular bundle fibers has received much attention, quantitative evaluation of the morphological transformation of vascular bundles associated with spatial distribution patterns has been limited. In this study deep learning models were used to determine quantitative changes in the distribution and morphology of vascular bundles in the culms of moso bamboo (Phyllostachys pubescens). A precise model for extracting vascular bundles from cross-sectional images was constructed using the U-Net model. Analyses of extracted vascular bundles from different internodes showed significant changes in vascular bundle distribution and morphology among internodes. Vascular bundles in lower internodes showed outer relative position and larger area than those in upper internodes. Aspect ratio and eccentricity indicate that vascular bundles in internodes near the base have more elliptical morphology, with a long axis in the radial direction. The variational autoencoder model using extracted vascular bundles enabled simulation of the morphological transformation of vascular bundles along with radial direction. These deep learning models enabled highly accurate quantification of vascular bundle morphologies, and will contribute to a further understanding of bamboo development as well as evaluation of the mechanical and physiological properties of bamboo.</div

The model of morphological and distribution variance of vascular bundles among internodes in a culm of moso bamboo.

Vascular bundles are expressed as brown area. The internode around base has vascular bundles with larger area, more elliptical shape and significantly radially longer morphology than those in upper internodes.</p

Characteristics of different internodes used for this study.

(A) original (left) and labeled (right) images obtained by Model 2. Scale = 1 mm. (B) area ratio of vascular bundles in each culm. Data are mean ± SD (n = 9) from three different culms. Different characters indicate significant differences (p < 0.05) by Tukey’s test.</p

Samples used in the present study for training U-Net models.

Samples used in the present study for training U-Net models.</p

Test images used in the present study to analyze by Model 2.

Test images used in the present study to analyze by Model 2.</p

Fig 3 -

Analyses example of vascular bundles of the 2nd internode extracted by U-Net model 2 (A-C). Radial position is relative position from epidermis (0) to pith cavity (1). (A) area (blue), perimeter (orange), convex area (green); (B) eccentricity (blue), aspect (tangential width/ radial width, orange), extent (vascular bundle area/area of bounding rectangle of vascular bundle, green); (C) area fraction of vascular bundles. D, Number of vascular bundles along with radial direction in the 2nd internode. Data are mean ± SD (n = 9) from three different culms. Different characters indicate significant differences (p < 0.05) by DSCF test.</p

Morphology and distributions of vascular bundles in different internodes.

Area (A), eccentricity (B), aspect (C, tangential width/radial width), relative distance (D) of vascular bundles extracted by Model 2. Box plots were made by extracted vascular bundles (n ≧ 357) from 9 blocks from 3 culms of different ages (see Table 2). Different letters indicate significant differences (p < 0.05, DSCF test).</p

Number of errors of connecting vascular bundles extracted by each U-Net model.

Number of errors of connecting vascular bundles extracted by each U-Net model.</p