Biomedical Imaging Using Optical-Based and Machine Learning Techniques

Dear Colleagues,

The invention of the optical microscope at the turn of the 17th century brought with it unprecedented image resolutions of biological samples and marked a breakthrough in the field of optical imaging. Since then, sophisticated inventions and technologies have emerged, and the field continues to grow. Optical radiation possesses intrinsic qualities that make it ideal for biomedical imaging. Firstly, it is non-ionizing, and hence, suitable for imaging applications requiring prolonged exposure to radiation and repeated tests. Secondly, it produces high-contrast biological images due to its high sensitivity to hemoglobin. Thirdly, its broad spectrum makes it an excellent candidate for spectroscopic imaging. Finally, optical radiation sources are relatively inexpensive and ubiquitous, thus expanding their range of applications.

Machine learning—or more specifically, deep learning—is a rapidly emerging new area of biomedical research and has yielded impressive disease diagnosis results in the fields of radiology and pathology. Deep learning employs computational models composed of a series of transforming and processing layers to learn representations of data with multiple levels of abstraction. Machine learning and deep learning techniques can be used to supplement optical imaging modalities to more accurately identify diseased and damaged tissue. Of the deep learning techniques, convolutional neural networks (CNN) are, by far, the most popular technique for biomedical image recognition, segmentation, and classification.

This Special Issue welcomes research articles and review papers on the development of novel optical-based and machine learning biomedical imaging systems or on their applications in biology and medicine. Areas of interest include, but are not limited to: (i) microscopy, (ii) optical coherent tomography, (iii) photoacoustic imaging, (iv) diffuse optical tomography, and (v) machine learning and deep learning for biomedical image analysis. In addition, works on optical imaging systems combined with other imaging modalities, such as ultrasound, X-rays, and MRI, are also welcome.

Dr. Patrick D. Kumavor
Dr. Chen Xu
Dr. Hassan S. Salehi
Guest Editors

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• biomedical and optical imaging
• spectroscopy, microscopy
• optical coherent tomography
• photoacoustic imaging
• diffuse optical tomography
• image coregistration
• machine learning
• deep learning
• image processing