Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Pittsburgh Center for Artificial Intelligence Innovation in Medical Imaging

We propose to create a medical imaging artificial intelligence (AI) center (name: Pittsburgh Center for Artificial Intelligence Innovation in Medical Imaging). AI is the new revolutionary technique for medical research and is reshaping tomorrow’s clinical practice in medical imaging (radiology and pathology). Our long-term vision is to build a center for innovative AI in clinical translational medical imaging by combining computational expertise and clinical resources across Pitt, UPMC, and CMU. The Center concept is a formalization of a group of researchers and clinicians that are united by the common theme: “building advanced and trustworthy imaging AI for clinical applications.” Our short-term plan is to assemble dedicated members from the School of Medicine, the School of Engineering, and the School of Computing and Information. We seek a Scaling grant from the Momentum Funds to foster collaborative activities of the Center between these three Pitt schools to provide the essential components of a competitive P41 (Biomedical Technology Resource Centers) center grant in 2 years. The National Institute of Biomedical Imaging and Bioengineering (NIBIB) P41 mechanism aligns with the overall vision of this initiative to develop specific AI imaging tools and to support the dissemination and commercialization pathways that are essential to bringing AI imaging tools to clinical practice. These projects will include key components: 1) Clinical need-driven medical imaging AI development and evaluation of tools, models, systems, and informatics, 2) Core imaging AI theory, methodology, and algorithm investigation, and 3) Linking imaging phenotypes to the biological (genomics and proteomics) underpinnings. To date, we have already 35 members for the Center. The Pitt Momentum Funds will provide critical scaling support to promote communication between the three Pitt schools to develop a competitive P41 grant application and a sustainable framework to ensure the clinical impact of these AI imaging tools

Editorial

The current issue of ARP contains an excellent article on artificial intelligence (AI) and its application in the Radiology domain that I would like to draw the reader’s attention. AI is not really a newborn technique and the concept and naming was set up by John McCarthy during the 50’s to define a system that could reason like humans, being self-sufficient in terms of cognitive and learning capabilities. For the last few years AI has gained momentum in many medical fields and one in the forefront is undoubtedly radiology. The massive use of medical imaging together with unparalleled computational power paved the way to big data analytics and data mining, which form the common ground for AI