scholarly journals A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation

2021 ◽  
Vol 12 ◽  
Author(s):  
Jonathon A. Gibbs ◽  
Lorna Mcausland ◽  
Carlos A. Robles-Zazueta ◽  
Erik H. Murchie ◽  
Alexandra J. Burgess
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Plant Performance ◽  
Plant Phenotyping ◽  
Maximal Conductance ◽  
Gas Fluxes ◽  
Fully Automatic ◽  
Automated Pipeline ◽  
Leaf Impressions ◽  
Deep Learning Network

Stomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant. The anatomy of stomata influences conductance properties with the maximal conductance rate, gsmax, calculated from density and size. However, current calculations of stomatal dimensions are performed manually, which are time-consuming and error prone. Here, we show how automated morphometry from leaf impressions can predict a functional property: the anatomical gsmax. A deep learning network was derived to preserve stomatal morphometry via semantic segmentation. This forms part of an automated pipeline to measure stomata traits for the estimation of anatomical gsmax. The proposed pipeline achieves accuracy of 100% for the distinction (wheat vs. poplar) and detection of stomata in both datasets. The automated deep learning-based method gave estimates for gsmax within 3.8 and 1.9% of those values manually calculated from an expert for a wheat and poplar dataset, respectively. Semantic segmentation provides a rapid and repeatable method for the estimation of anatomical gsmax from microscopic images of leaf impressions. This advanced method provides a step toward reducing the bottleneck associated with plant phenotyping approaches and will provide a rapid method to assess gas fluxes in plants based on stomata morphometry.

Semantic segmentation for plant phenotyping using advanced deep learning pipelines

2021 ◽  
Author(s):  
Pullalarevu Karthik ◽  
Mansi Parashar ◽  
S. Sofana Reka ◽  
Kumar T. Rajamani ◽  
Mattias P. Heinrich
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Plant Phenotyping

scholarly journals A Novel Point Cloud Encoding Method Based on Local Information for 3D Classification and Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2501 ◽  
Author(s):  
Yanan Song ◽  
Liang Gao ◽  
Xinyu Li ◽  
Weiming Shen
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Semantic Segmentation ◽  
Local Information ◽  
Local Region ◽  
Feature Representation ◽  
Learning Network ◽  
Feature Representations ◽  
Encoding Method ◽  
Deep Learning Network

Deep learning is robust to the perturbation of a point cloud, which is an important data form in the Internet of Things. However, it cannot effectively capture the local information of the point cloud and recognize the fine-grained features of an object. Different levels of features in the deep learning network are integrated to obtain local information, but this strategy increases network complexity. This paper proposes an effective point cloud encoding method that facilitates the deep learning network to utilize the local information. An axis-aligned cube is used to search for a local region that represents the local information. All of the points in the local region are available to construct the feature representation of each point. These feature representations are then input to a deep learning network. Two well-known datasets, ModelNet40 shape classification benchmark and Stanford 3D Indoor Semantics Dataset, are used to test the performance of the proposed method. Compared with other methods with complicated structures, the proposed method with only a simple deep learning network, can achieve a higher accuracy in 3D object classification and semantic segmentation.

scholarly journals Incorporating the Breast Imaging Reporting and Data System Lexicon with a Fully Convolutional Network for Malignancy Detection on Breast Ultrasound

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 66
Author(s):  
Yung-Hsien Hsieh ◽  
Fang-Rong Hsu ◽  
Seng-Tong Dai ◽  
Hsin-Ya Huang ◽  
Dar-Ren Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Imaging ◽  
Area Under The Curve ◽  
Semantic Segmentation ◽  
Breast Ultrasound ◽  
Data System ◽  
Ultrasound Images ◽  
Convolutional Network ◽  
Learning Network ◽  
Deep Learning Network

In this study, we applied semantic segmentation using a fully convolutional deep learning network to identify characteristics of the Breast Imaging Reporting and Data System (BI-RADS) lexicon from breast ultrasound images to facilitate clinical malignancy tumor classification. Among 378 images (204 benign and 174 malignant images) from 189 patients (102 benign breast tumor patients and 87 malignant patients), we identified seven malignant characteristics related to the BI-RADS lexicon in breast ultrasound. The mean accuracy and mean IU of the semantic segmentation were 32.82% and 28.88, respectively. The weighted intersection over union was 85.35%, and the area under the curve was 89.47%, showing better performance than similar semantic segmentation networks, SegNet and U-Net, in the same dataset. Our results suggest that the utilization of a deep learning network in combination with the BI-RADS lexicon can be an important supplemental tool when using ultrasound to diagnose breast malignancy.

scholarly journals DEANet: Dual Encoder with Attention Network for Semantic Segmentation of Remote Sensing Imagery

2021 ◽  
Vol 13 (19) ◽  
pp. 3900
Author(s):  
Haoran Wei ◽  
Xiangyang Xu ◽  
Ni Ou ◽  
Xinru Zhang ◽  
Yaping Dai
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Feature Maps ◽  
Attention Network ◽  
Remote Sensing Imagery ◽  
Training Strategy ◽  
Sensing Technology ◽  
Deep Learning Network ◽  
Public Datasets

Remote sensing has now been widely used in various fields, and the research on the automatic land-cover segmentation methods of remote sensing imagery is significant to the development of remote sensing technology. Deep learning methods, which are developing rapidly in the field of semantic segmentation, have been widely applied to remote sensing imagery segmentation. In this work, a novel deep learning network—Dual Encoder with Attention Network (DEANet) is proposed. In this network, a dual-branch encoder structure, whose first branch is used to generate a rough guidance feature map as area attention to help re-encode feature maps in the next branch, is proposed to improve the encoding ability of the network, and an improved pyramid partial decoder (PPD) based on the parallel partial decoder is put forward to make fuller use of the features form the encoder along with the receptive filed block (RFB). In addition, an edge attention module using the transfer learning method is introduced to explicitly advance the segmentation performance in edge areas. Except for structure, a loss function composed with the weighted Cross Entropy (CE) loss and weighted Union subtract Intersection (UsI) loss is designed for training, where UsI loss represents a new region-based aware loss which replaces the IoU loss to adapt to multi-classification tasks. Furthermore, a detailed training strategy for the network is introduced as well. Extensive experiments on three public datasets verify the effectiveness of each proposed module in our framework and demonstrate that our method achieves more excellent performance over some state-of-the-art methods.

scholarly journals SA-Net: A scale-attention network for medical image segmentation

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0247388
Author(s):  
Jingfei Hu ◽  
Hua Wang ◽  
Jie Wang ◽  
Yunqi Wang ◽  
Fang He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Image ◽  
Multiple Scales ◽  
Medical Images ◽  
Semantic Segmentation ◽  
Medical Image Segmentation ◽  
Retinal Images ◽  
Multi Scale ◽  
Multiple Datasets ◽  
Deep Learning Network

Semantic segmentation of medical images provides an important cornerstone for subsequent tasks of image analysis and understanding. With rapid advancements in deep learning methods, conventional U-Net segmentation networks have been applied in many fields. Based on exploratory experiments, features at multiple scales have been found to be of great importance for the segmentation of medical images. In this paper, we propose a scale-attention deep learning network (SA-Net), which extracts features of different scales in a residual module and uses an attention module to enforce the scale-attention capability. SA-Net can better learn the multi-scale features and achieve more accurate segmentation for different medical image. In addition, this work validates the proposed method across multiple datasets. The experiment results show SA-Net achieves excellent performances in the applications of vessel detection in retinal images, lung segmentation, artery/vein(A/V) classification in retinal images and blastocyst segmentation. To facilitate SA-Net utilization by the scientific community, the code implementation will be made publicly available.

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