Dataset Information

High-Throughput in situ Root Image Segmentation Based on the Improved DeepLabv3+ Method.

ABSTRACT: 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 in situ images of cotton roots obtained with a micro root window root system monitoring system. Segmentation performance of the proposed method utilizing WinRHIZO Tron MF analysis was assessed using these images. After 80 epochs of training, the final verification set F1-score, recall, and precision were 0.9773, 0.9847, and 0.9702, respectively. The Spearman rank correlation between the manually obtained Rhizotrons manual segmentation root length and automated root length was 0.9667 (p < 10-8), with r 2 = 0.9449. Based on the comparison of our segmentation results with those of traditional manual and U-net segmentation methods, this novel method can more accurately segment root systems in complex soil environments. Thus, using the improved DeepLabv3+ to segment root systems based on micro-root images is an effective method for accurately and quickly segmenting root systems in a homogeneous soil environment and has clear advantages over traditional manual segmentation.

SUBMITTER: Shen C

PROVIDER: S-EPMC7604297 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

High-Throughput <i>in situ</i> Root Image Segmentation Based on the Improved DeepLabv3+ Method.

Shen Chen C Liu Liantao L Zhu Lingxiao L Kang Jia J Wang Nan N Shao Limin L

Frontiers in plant science 20201019

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 obtaine ...[more]

PMID: 33193519

Similar Datasets

Project description:Feline coronaviruses (FCoV) are members of the alphacoronavirus genus that are further characterized by serotype (types I and II) based on the antigenicity of the spike (S) protein and by pathotype based on the associated clinical conditions. Feline enteric coronaviruses (FECV) are associated with the vast majority of infections and are typically asymptomatic. Within individual animals, FECV can mutate and cause a severe and usually fatal disease called feline infectious peritonitis (FIP), the leading infectious cause of death in domestic cat populations. There are no approved antiviral drugs or recommended vaccines to treat or prevent FCoV infection. The plaque reduction neutralization test (PRNT) traditionally employed to assess immune responses and to screen therapeutic and vaccine candidates is time-consuming, low-throughput, and typically requires 2-3 days for the formation and manual counting of cytolytic plaques. Host cells are capable of carrying heavy viral burden in the absence of visible cytolytic effects, thereby reducing the sensitivity of the assay. In addition, operator-to-operator variation can generate uncertainty in the results and digital records are not automatically created. To address these challenges we developed a novel high-throughput viral microneutralization assay, with quantification of virus-infected cells performed in a plate-based image cytometer. Host cell seeding density, microplate surface coating, virus concentration and incubation time, wash buffer and fluorescent labeling were optimized. Subsequently, this FCoV viral neutralization assay was used to explore immune correlates of protection using plasma from naturally FECV-infected cats. We demonstrate that the high-throughput viral neutralization assay using the Celigo Image Cytometer provides a robust and efficient method for the rapid screening of therapeutic antibodies, antiviral compounds, and vaccines. This method can be applied to various viral infectious diseases to accelerate vaccine and antiviral drug discovery and development.

Project description:Image contrast enhancement uses the object intensity transformation function to maximize the amount of information to enhance an image. In this paper, the image enhancement problem is regarded as an optimization problem, and the particle swarm algorithm is used to obtain the optimal solution. First, an improved particle swarm optimization algorithm is proposed. In this algorithm, individual optimization, local optimization, and global optimization are used to adjust the particle's flight direction. In local optimization, the topology is used to induce comparison and communication between particles. The sparse penalty term in speed update formula is added to adjust the sparsity of the algorithm and the size of the solution space. Second, the three channels of the color images R, G, and B are represented by a quaternion matrix, and an improved particle swarm algorithm is used to optimize the transformation parameters. Finally, contrast and brightness elements are added to the fitness function. The fitness function is used to guide the particle swarm optimization algorithm to optimize the parameters in the transformation function. This paper verifies via two experiments. First, improved particle swarm algorithm is simulated and tested. By comparing the average values of the four algorithms under the three types of 6 test functions, the average value is increased by at least 15 times in the single-peak 2 test functions: in the multi-peak and multi-peak fixed-dimension 4 test functions, this paper can always search for the global optimal solution, and the average value is either the same or at least 1.3 times higher. Second, the proposed algorithm is compared with other evolutionary algorithms to optimize contrast enhancement, select images in two different data sets, and calculate various evaluation indicators of different algorithms under different images. The optimal value is the algorithm in this paper, and the performance indicators are at least a 5% increase and a minimum 15% increase in algorithm running time. Final results show that the effects the proposed algorithm have obvious advantages in both subjective and qualitative aspects.

Dataset Information

High-Throughput in situ Root Image Segmentation Based on the Improved DeepLabv3+ Method.

Publications

High-Throughput <i>in situ</i> Root Image Segmentation Based on the Improved DeepLabv3+ Method.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets