Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation

Abstract

This paper develops an approach to perform binary semantic segmentation on Arabidopsis thaliana root images for plant root phenotyping using a conditional generative adversarial network (cGAN) to address pixel-wise class imbalance. Specifically, we use Pix2PixHD, an image-to-image translation cGAN, to generate realistic and high resolution images of plant roots and annotations similar to the original dataset. Furthermore, we use our trained cGAN to triple the size of our original root dataset to reduce pixel-wise class imbalance. We then feed both the original and generated datasets into SegNet to semantically segment the root pixels from the background. Furthermore, we postprocess our segmentation results to close small, apparent gaps along the main and lateral roots. Lastly, we present a comparison of our binary semantic segmentation approach with the state-of-the-art in root segmentation. Our efforts demonstrate that cGAN can produce realistic and high resolution root images, reduce pixel-wise class imbalance, and our segmentation model yields high testing accuracy (of over 99%), low cross entropy error (of less than 2%), high Dice Score (of near 0.80), and low inference time for near real-time processing.

Plant phenotyping relevance

植物根画像から根領域を抽出するセマンティックセグメンテーション手法を開発・比較しており、根フェノタイピングの取得・解析方法が研究の中心である。

Code and data availability

The paper's own analysis code (Pix2PixHD-based cGAN training and SegNet segmentation) is only promised for future release on the authors' lab website, with no public URL provided. The two GitHub repositories referenced (NVIDIA/pix2pixHD and aizawan/segnet) are generic third-party codebases that inspired the work, not a

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