A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements
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Abstract (arXiv)
With the acceleration of urbanization and living standards, microorganisms play increasingly important roles in industrial production, bio-technique, and food safety testing. Microorganism biovolume measurements are one of the essential parts of microbial analysis. However, traditional manual measurement methods are time-consuming and challenging to measure the characteristics precisely. With the development of digital image processing techniques, the characteristics of the microbial population can be detected and quantified. The changing trend can be adjusted in time and provided a basis for the improvement. The applications of the microorganism biovolume measurement method have developed since the 1980s. More than 62 articles are reviewed in this study, and the articles are grouped by digital image segmentation methods with periods. This study has high research significance and application value, which can be referred to microbial researchers to have a comprehensive understanding of microorganism biovolume measurements using digital image analysis methods and potential applications.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 18 of its 158 references that are in the map. First posted on arXiv 18 Feb 2022.
Builds on
18 of 158 references.
References · 18
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,043
- 2017Squeeze-and-Excitation Networkscited by 4,772
- 2015Spatial Transformer Networkscited by 1,425
- 2017A Review on Deep Learning Techniques Applied to Semantic Segmentationcited by 122
- 2019Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networkcited by 56
- 2021A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approachescited by 44
- 2019Object Counting and Instance Segmentation with Image-level Supervisioncited by 35
- 2020A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networkscited by 25
- 2021DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniquescited by 23
- 2021A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approachescited by 20
- 2021Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: from convolutional neural networks to visual transformerscited by 16
- 2021Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformercited by 14
6 more
- 2020A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysiscited by 13
- 2021A New Pairwise Deep Learning Feature For Environmental Microorganism Image Analysiscited by 12
- 2021A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformerscited by 9
- 2020A SARS-CoV-2 Microscopic Image Dataset with Ground Truth Images and Visual Featurescited by 6
- 2020A Review on Near Duplicate Detection of Images using Computer Vision Techniquescited by 4
- 2021EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluationcited by 4
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Citing papers per year: 2022: 1.
1 paper.