scholarly journals Research on image analysis technology for determining alkali active component content of alkali in low-grade metamorphic rock based on deep learning

2021 ◽  
Vol 861 (2) ◽  
pp. 022067
Author(s):  
Haotian Zou ◽  
Qian Yang ◽  
Yi Liu ◽  
Xiaoxiong Wang
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Active Component ◽  
Metamorphic Rock ◽  
Low Grade ◽  
Component Content

KloudNet: Deep Learning for Sky Image Analysis and Irradiance Forecasting

2019 ◽  
pp. 535-551 ◽  
Author(s):  
Dinesh Pothineni ◽  
Martin R. Oswald ◽  
Jan Poland ◽  
Marc Pollefeys
Keyword(s):  
Image Analysis ◽  
Deep Learning

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

scholarly journals Trends in Deep Learning for Medical Hyperspectral Image Analysis

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Uzair Khan ◽  
Sidike Paheding ◽  
Colin Elkin ◽  
Vijay Devabhaktuni
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Hyperspectral Image ◽  
Hyperspectral Image Analysis

scholarly journals Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2866
Author(s):  
Fernando Navarro ◽  
Hendrik Dapper ◽  
Rebecca Asadpour ◽  
Carolin Knebel ◽  
Matthew B. Spraker ◽  
...  
Keyword(s):  
Deep Learning ◽  
Soft Tissue ◽  
Mr Imaging ◽  
Characteristic Curve ◽  
External Validation ◽  
Soft Tissue Sarcomas ◽  
Treatment Decision ◽  
Receiver Operator Characteristic Curve ◽  
Low Grade ◽  
Tumor Grading

Background: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. Methods: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. Results: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. Conclusions: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.

Applications of Deep Learning in Microscopy Image Analysis

2021 ◽  
pp. 104523
Author(s):  
Zhichao Liu ◽  
Luhong Jin ◽  
Jincheng Chen ◽  
Qiuyu Fang ◽  
Sergey Ablameyko ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Microscopy Image ◽  
Microscopy Image Analysis

scholarly journals Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

2021 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
Tirivangani Magadza ◽  
Serestina Viriri
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Brain Tumor ◽  
Quantitative Analysis ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Building Blocks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

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