期刊
BIOPHYSICAL REVIEWS
卷 14, 期 2, 页码 463-481出版社
SPRINGERNATURE
DOI: 10.1007/s12551-022-00949-3
关键词
Optical microscopy; Image processing; Machine learning; Deep learning
类别
资金
- We thank Manipal School of Life Sciences (MSLS), Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India, for providing the infrastructure needed.
- Manipal School of Life Sciences
- Manipal Academy of Higher Education
- Manipal, Karnataka, India
Optical microscopy has become increasingly important in biomedical research, and deep learning-based image processing plays a key role in its advancement. By utilizing automated image analysis methods, deep learning improves processing speed and reduces error accumulation. It has proven successful in various applications, such as image classification, segmentation, and resolution enhancement in smartphone-based microscopy.
Optical microscopy has emerged as a key driver of fundamental research since it provides the ability to probe into imperceptible structures in the biomedical world. For the detailed investigation of samples, a high-resolution image with enhanced contrast and minimal damage is preferred. To achieve this, an automated image analysis method is preferable over manual analysis in terms of both speed of acquisition and reduced error accumulation. In this regard, deep learning (DL)-based image processing can be highly beneficial. The review summarises and critiques the use of DL in image processing for the data collected using various optical microscopic techniques. In tandem with optical microscopy, DL has already found applications in various problems related to image classification and segmentation. It has also performed well in enhancing image resolution in smartphone-based microscopy, which in turn enablse crucial medical assistance in remote places.
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