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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,...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823511/ https://www.ncbi.nlm.nih.gov/pubmed/36616905 http://dx.doi.org/10.3390/s23010309 |
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author | Thesma, Vaishnavi Mohammadpour Velni, Javad |
author_facet | Thesma, Vaishnavi Mohammadpour Velni, Javad |
author_sort | Thesma, Vaishnavi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9823511 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98235112023-01-08 Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation Thesma, Vaishnavi Mohammadpour Velni, Javad Sensors (Basel) Article 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. MDPI 2022-12-28 /pmc/articles/PMC9823511/ /pubmed/36616905 http://dx.doi.org/10.3390/s23010309 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Thesma, Vaishnavi Mohammadpour Velni, Javad Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation |
title | Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation |
title_full | Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation |
title_fullStr | Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation |
title_full_unstemmed | Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation |
title_short | Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation |
title_sort | plant root phenotyping using deep conditional gans and binary semantic segmentation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823511/ https://www.ncbi.nlm.nih.gov/pubmed/36616905 http://dx.doi.org/10.3390/s23010309 |
work_keys_str_mv | AT thesmavaishnavi plantrootphenotypingusingdeepconditionalgansandbinarysemanticsegmentation AT mohammadpourvelnijavad plantrootphenotypingusingdeepconditionalgansandbinarysemanticsegmentation |