Improvements in contemporary machine learning and computer vision architectures have drastically increased the available tools in biological analysis, particularly in feature segmentation in 2D images and 3D models. A major obstacle for building machine learning architectures is associated with generating large amounts of training data, a process which requires significant time and processing resources. Recent software tools allow for an interactive training approach that show great promise in reducing the time and effort needed to generate training data. This study analyzes cross-sectional images of Soybean stems obtained via Laser Ablation Tomography (LATscan) using two open-source interactive machine-learning tools.Magnetic resonance imaging and x-ray tomography are slow and costly, with significant tradeoffs between resolution, speed, and volume. Additionally, contrasting agents are required to differentiate features.LATscan utilizes an ultrafast, ultraviolet pulsed laser to continuously ablate thin surface layers of samples, and these surfaces are simultaneously imaged as the sample is fed into the laser ablation plane. The resulting data are processed using machine learning techniques to identify structural and compositional features at micron resolution. The images are then stacked, further processed, and reconstructed into high-resolution volume renderings that can be analyzed, quantified, segmented, and virtually dissected. The 3D renderings obtained are in full, natural color that results from the interaction of the materials with the ultraviolet laser during ablation. The application of LATscan to both biological and nonbiological samples has shown excellent results in the fields of botany, entomology, life sciences, material science, and pharmacology, among many others.The most successful roll-out of the technology has been in the agrochemical industry, specifically in studies involving high-throughput phenotyping, root–soil, and plant–pest interactions, biomimetics, and pesticide efficacy. The technology has also offered exciting results in the life sciences, and the combination of the visualization with 3D chemical mapping could be a fundamental research and diagnostic tool.Rootpainter and Ilastik interactive software tools are trained to identify Xylem conduits depicted in the LATscan images, from which count and area data are collected. This resulting information is then compared to identify any possible bias in the algorithms and training methods. The results show effective creation of predictive models with a low amount of training data, thus presenting plant scientists with a powerful new tool for quantifying plant features that can be used for high impact phenotyping studies.&nbsp;The quantification of xylem provides insight into the constitution of the plants as the xylem vessel provides both mechanical structure and water delivery upward to sites of photosynthesis. Additionally, because growth ring features are evident in the LATscan images, xylem vessel quantity may be seen with respect to age. This processing methodology will drastically increase the information available to crop scientists, allowing them to improve crop performance.&nbsp;&nbsp;

Quantification of Vascular Features in Soybean Stems via Laser Ablation Tomography (Latscan)

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

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