Melanthota Sindhoora Kaniyala, Gopal Dharshini, Chakrabarti Shweta, Kashyap Anirudh Ameya, Radhakrishnan Raghu, Mazumder Nirmal
Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India.
Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India.
Biophys Rev. 2022 Apr 6;14(2):463-481. doi: 10.1007/s12551-022-00949-3. eCollection 2022 Apr.
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.
光学显微镜已成为基础研究的关键驱动力，因为它能够探测生物医学领域中难以察觉的结构。对于样品的详细研究，人们更倾向于具有增强对比度和最小损伤的高分辨率图像。为此，在采集速度和减少误差积累方面，自动图像分析方法优于手动分析。在这方面，基于深度学习（DL）的图像处理可能非常有益。这篇综述总结并评论了深度学习在图像处理中的应用，这些图像数据是使用各种光学显微镜技术收集的。与光学显微镜相结合，深度学习已经在与图像分类和分割相关的各种问题中得到应用。它在提高基于智能手机的显微镜的图像分辨率方面也表现出色，这反过来又能在偏远地区提供关键的医疗帮助。
