Dear Colleagues,

Artificial intelligence (AI) present in many aspects of our lives involves various algorithmic learning techniques, including machine learning (ML), its subcategory deep learning (DL), and natural language processing (NLP). ML allows computers to learn from data either through supervised, unsupervised, semi-supervised, or reinforcement learning. Techniques and applications of computational intelligence in medical imaging explores how intelligent computing brings enormous benefit to existing technology in medical image processing and improves medical imaging research. Clinical applications of AI in medical imaging encompass general radiography, computed tomography (CT), magnetic resonance imaging (MRI), fluoroscopy and radiation therapy. As the initial tool for solving clinical questions, researches with AI critical findings of general radiography are automatically flagged and prioritised.

Thus, we are interested in diagnostics approaches for detecting osteoarthritis, classifying wound images, extracting the boundaries of skin lesions, automatic segmentation and diagnosis of bone scintigraphy, analyzing and segmenting prostate in transrectal ultrasound images, 3-D medical image segmentation, the reconstruction of SPECT and PET tomographic images, image fusion, and simulating fluid flows for cardiovascular applications.

For this, it is important to collect the experiences of different developed methods and strategies that have been implemented and consider their impacts. Research articles and reviews in this area of study are welcome.

We look forward to receiving your contributions.

Dr. Yinghua Fu

Section editor