Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation

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.

Automated summary

This paper proposes an approach for binary semantic segmentation of Arabidopsis thaliana root images using a conditional generative adversarial network (cGAN) to address pixel-wise class imbalance. By utilizing Pix2PixHD, a cGAN for image translation, realistic and high resolution root images and annotations are generated to expand the original dataset. The original and generated datasets are then fed into SegNet for semantic segmentation, with additional post-processing to close small gaps along the root structure. Comparative analysis demonstrates that the cGAN approach produces realistic root images, reduces class imbalance, and achieves high accuracy, low error, high Dice Score, and low inference time for real-time processing.