Azimuthal angle calculation from retardation imaging, and selective radial and tangential wall extraction from azimuthal angle distributions.

(A) Definitions of azimuthal angle, and radial and tangential directions in the present experimental condition. Radial and tangential planes are colored green and yellow, respectively. (B) Azimuthal angle distribution (left, purple line) of a hexagonal-shaped tracheid (right). Tangential and radial walls are defined as pixels whose azimuthal angle is near to 45° (left: Area covered by the black curve; right: Yellow pixels, directions of yellow double-headed arrows) or the remaining parts (right: Green pixels).</p

Example of image concatenation applied to a set of retardation images.

The reconstructed retardation image comprised 72 partly overlapping retardation images in total (9 columns × 8 rows). Each image contained 66% overlapping regions with adjacent image patches in the horizontal and vertical directions.</p

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

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

Selective detection of S₂ or S₁+S₃ layers.

(A) S2 detection and (B) S1+S3 detection in a latewood, (D) S2 detection and (E) S1+S3 detection in an earlywood. (C) and (F) are merged images of (A) and (B), and (D) and (E), respectively. Red and green dots correspond to the detected S2 (local-intensity minima) and S1+S3 (local-intensity maxima) layers’ contributions, respectively. Bars, 10 μm.</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

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

Normalized intra-annual transitional behaviors of a portion of the anatomical parameters.

(PDF)</p

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