Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy

Abstract

The recent developments in artificial intelligence have the potential to facilitate new research methods in ecology. Especially Deep Convolutional Neural Networks (DCNNs) have been shown to outperform other approaches in automatic image analyses. Here we apply a DCNN to facilitate quantitative wood anatomical (QWA) analyses, where the main challenges reside in the detection of a high number of cells, in the intrinsic variability of wood anatomical features, and in the sample quality. To properly classify and interpret features within the images, DCNNs need to undergo a training stage. We performed the training with images from transversal wood anatomical sections, together with manually created optimal outputs of the target cell areas. The target species included an example for the most common wood anatomical structures: four conifer species; a diffuse-porous species, black alder (Alnus glutinosa L.); a diffuse to semi-diffuse-porous species, European beech (Fagus sylvatica L.); and a ring-porous species, sessile oak (Quercus petraea Liebl.). The DCNN was created in Python with Pytorch, and relies on a Mask-RCNN architecture. The developed algorithm detects and segments cells, and provides information on the measurement accuracy. To evaluate the performance of this tool we compared our Mask-RCNN outputs with U-Net, a model architecture employed in a similar study, and with ROXAS, a program based on traditional image analysis techniques. First, we evaluated how many target cells were correctly recognized. Next, we assessed the cell measurement accuracy by evaluating the number of pixels that were correctly assigned to each target cell. Overall, the "learning process" defining artificial intelligence plays a key role in overcoming the issues that are usually manually solved in QWA analyses. Mask-RCNN is the model that better detects which are the features characterizing a target cell when these issues occur. In general, U-Net did not attain the other algorithms' performance, while ROXAS performed best for conifers, and Mask-RCNN showed the highest accuracy in detecting target cells and segmenting lumen areas of angiosperms. Our research demonstrates that future software tools for QWA analyses would greatly benefit from using DCNNs, saving time during the analysis phase, and providing a flexible approach that allows model retraining.

Keywords: F1 score; ROXAS; artificial intelligence; deep learning; lumen area; wood anatomy.

FIGURE 1

Patch from an original beech thin section, error map, and related legend. The error map is the visual comparison between the algorithm computation and the ground truth, and it helps in the visual assessment of the algorithms’ performance.

FIGURE 2

Flowchart of the comparison process. To obtain a meaningful result, the output from the algorithms has to be compared to the ground truth, a manually segmented image for every image of the groups investigated. Mask-RCNN output was then compared to the one of U-Net and ROXAS in terms of cell instance (cell detected: yes/no) and lumen area accuracy (how accurately the lumen area was detected).

FIGURE 3

Flowchart summarizing how performance of the considered approaches was assessed.

FIGURE 4

Histograms of the missed cells (FN value) per groups: conifer, alder, beech, and oak. The count in the legend refers to the FN instances that were recorded in total per algorithm for each species, while the histograms show the respective size-frequency distributions.

FIGURE 5

Output comparison between Mask-RCNN and ROXAS on examples of overlaying dust particle (A), paraffin drops (B), and stains of coloring solutions (C). Error legend can be found in Figure 1. The FN cell instance are marked in blue, and in ROXAS outputs they exactly correspond to the area interested by the (A–C) issue.

FIGURE 6

Output comparison on vessel identification. Original alder image from the testing dataset, Mask-RCNN error map, and ROXAS error map. When the perforation plate is strongly visible, there is a high chance that one single vessel is wrongly recognized as two separate ones.

FIGURE 7

Box plot showing the frequency distribution of the F1 score for lumen area, which allows a comparison of the three algorithms (Mask-RCNN, U-Net, and ROXAS) for each tree-species group. The blue values represent the fraction of cells reaching and surpassing the F1 score threshold of 0.9.

FIGURE 8

(A) Example of lumen area underestimation, the dark green area represents the FN pixels. (B) Example of lumen area overestimation, the orange area represents the FP pixels.