Frontiers in Plant Science ( IF 4.1 ) Pub Date : 2020-09-22 , DOI: 10.3389/fpls.2020.576791 Chen Shen , Liantao Liu , Lingxiao Zhu , Jia Kang , Nan Wang , Limin Shao
The Rhizotrons method is an important means of detecting dynamic growth and development phenotypes of plant roots. However, the segmentation of root images is a critical obstacle restricting further development of this method. At present, researchers mostly use direct manual drawings or software-assisted manual drawings to segment root systems for analysis. Root systems can be segmented from root images obtained by the Rhizotrons method, and then, root system lengths and diameters can be obtained with software. This type of image segmentation method is extremely inefficient and very prone to human error. Here, we investigate the effectiveness of an automated image segmentation method based on the DeepLabv3+ convolutional neural network (CNN) architecture to streamline such measurements. We have improved the upsampling portion of the DeepLabv3+ network and validated it using
中文翻译:
基于改进的DeepLabv3 +方法的高通量原位根图像分割
根瘤菌方法是检测植物根系动态生长和发育表型的重要手段。然而,根图像的分割是限制该方法进一步发展的关键障碍。目前,研究人员大多使用直接的手动图或软件辅助的手动图来分割根系统以进行分析。根系可以从通过Rhizotrons方法获得的根图像中分割出来,然后可以使用软件获得根系的长度和直径。这种类型的图像分割方法效率极低,极易出现人为错误。在这里,我们研究了基于DeepLabv3 +卷积神经网络(CNN)架构的自动图像分割方法来简化此类测量的有效性。