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Artificial Intelligence in Medical Imaging

Call for papers

. © © metamorworksAt a time where artificial intelligence (AI), machine and deep learning techniques are advancing rapidly, it becomes increasingly important to showcase developments in the field of image processing and analysis, as well as AI contributions to diagnostics. 
We are hoping this collection will further the understanding of AI in medical imaging, highlight its versatility and applications, and break down barriers that still exist in the field. 

BMC Medical Imaging invites you to submit to our new collection on "Artificial Intelligence in Medical Imaging". This collection will be closing in summer 2021. 

This collection of articles has not been sponsored and articles undergo the journal’s standard peer-review process overseen by our Guest Editors, Prof. Alexander Wong (University of Waterloo) and Prof. Xiaobo Qu (Xiamen University).  

Submission is open to everyone. Before submitting your manuscript, please ensure you have carefully read the submission guidelines for BMC Medical Imaging. Information about our article-processing charges and waivers can be found here.
To ensure consideration in our collection, please submit your article through the Editorial Manager system

Data sets and descriptions relevant to the collection will be considered in BMC Research Notes as Data Notes. You can find out more about this article type here. This type of content will be published in BMC Research Notes and included in the final collection.  

  1. Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed intern...

    Authors: Iben Fasterholdt, Mohammad Naghavi-Behzad, Benjamin S. B. Rasmussen, Tue Kjølhede, Mette Maria Skjøth, Malene Grubbe Hildebrandt and Kristian Kidholm
    Citation: BMC Medical Imaging 2022 22:187

    The Correction to this article has been published in BMC Medical Imaging 2023 23:13

  2. Recent developments to segment and characterize the regions of interest (ROI) within medical images have led to promising shape analysis studies. However, the procedures to analyze the ROI are arbitrary and va...

    Authors: Esteban Fernández, Shengjie Yang, Sy Han Chiou, Chul Moon, Cong Zhang, Bo Yao, Guanghua Xiao and Qiwei Li
    Citation: BMC Medical Imaging 2022 22:129
  3. Both early detection and severity assessment of liver trauma are critical for optimal triage and management of trauma patients. Current trauma protocols utilize computed tomography (CT) assessment of injuries ...

    Authors: Negar Farzaneh, Erica B. Stein, Reza Soroushmehr, Jonathan Gryak and Kayvan Najarian
    Citation: BMC Medical Imaging 2022 22:39
  4. Magnetic resonance imaging (MRI) is an effective auxiliary diagnostic method in clinical medicine, but it has always suffered from the problem of long acquisition time. Compressed sensing and parallel imaging ...

    Authors: Gushan Zeng, Yi Guo, Jiaying Zhan, Zi Wang, Zongying Lai, Xiaofeng Du, Xiaobo Qu and Di Guo
    Citation: BMC Medical Imaging 2021 21:195
  5. Automated language analysis of radiology reports using natural language processing (NLP) can provide valuable information on patients’ health and disease. With its rapid development, NLP studies should have tr...

    Authors: Emma M. Davidson, Michael T. C. Poon, Arlene Casey, Andreas Grivas, Daniel Duma, Hang Dong, Víctor Suárez-Paniagua, Claire Grover, Richard Tobin, Heather Whalley, Honghan Wu, Beatrice Alex and William Whiteley
    Citation: BMC Medical Imaging 2021 21:142
  6. Functional imaging especially the SPECT bone scintigraphy has been accepted as the effective clinical tool for diagnosis, treatment, evaluation, and prevention of various diseases including metastasis. However...

    Authors: Qiang Lin, Chuangui Cao, Tongtong Li, Zhengxing Man, Yongchun Cao and Haijun Wang
    Citation: BMC Medical Imaging 2021 21:122
  7. Lung region segmentation is an important stage of automated image-based approaches for the diagnosis of respiratory diseases. Manual methods executed by experts are considered the gold standard, but it is time...

    Authors: Michael Osadebey, Hilde K. Andersen, Dag Waaler, Kristian Fossaa, Anne C. T. Martinsen and Marius Pedersen
    Citation: BMC Medical Imaging 2021 21:112
  8. One challenge to train deep convolutional neural network (CNNs) models with whole slide images (WSIs) is providing the required large number of costly, manually annotated image regions. Strategies to alleviate...

    Authors: Sebastian Otálora, Niccolò Marini, Henning Müller and Manfredo Atzori
    Citation: BMC Medical Imaging 2021 21:77

Meet the Guest Editors

Xiaobo Qu

New Content Item (1)Xiaobo Qu received his B.S. and Ph.D. degrees in communication engineering from Xiamen University, P.R. China, in 2006 and 2011, respectively. From 2009 to 2011, he was Visiting Scholar in the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign. In 2014, he was Visiting Scientist at the Swedish NMR Centre, University of Gothenburg, Sweden. From 2018 to 2019, he was Visiting Scholar in the Department of Radiology, University of Washington at Seattle. Since 2012, he has been a faculty member of Xiamen University, where he is currently Professor in the Department of Electronic Science, leading the computational sensing lab ( He is also affiliated with the Research Center of Magnetic Resonance and Medical Imaging, the National Institute for Data Science in Health and Medicine, and the Research Center for Molecular Imaging and Translational Medicine. He has published a series of papers in prime journals in the fields of medical imaging, biomedical engineering, and signal processing, such as IEEE Trans. Medical Imaging, IEEE Trans. Signal Processing, IEEE Trans. Biomedical Engineering, Medical Image Analysis, Angewandte Chemie International Edition, etc. His research interests include magnetic resonance spectroscopy and imaging, computational imaging, machine learning, and artificial intelligence. 
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Alexander Wong

New Content Item (1)Alexander Wong, P.Eng., is currently the Canada Research Chair in Artificial Intelligence and Medical Imaging, Member of the College of the Royal Society of Canada, co-director of the Vision and Image Processing Research Group, an associate professor in the Department of Systems Design Engineering at the University of Waterloo, and Co-founder and Chief Scientist of DarwinAI.  He has published over 570 refereed journal and conference papers, as well as patents, in various fields such as computational imaging, artificial intelligence, machine learning, and computer vision.  In the area of computational imaging, his focus is on integrative computational imaging systems for biomedical imaging (inventor/co-inventor of Correlated Diffusion Imaging, Compensated Magnetic Resonance Imaging, Spectral Light-field Fusion Micro-tomography, Compensated Ultrasound Imaging, Coded Hemodynamic Imaging, High-throughput Computational Slits, Spectral Demultiplexing Imaging, and Parallel Epi-Spectropolarimetric Imaging). In the area of artificial intelligence, his focus is on operational artificial intelligence (co-inventor/inventor of Generative Synthesis, evolutionary deep intelligence, Deep Bayesian Residual Transform, Discovery Radiomics, and random deep intelligence via deep-structured fully-connected graphical models).  He has received numerous awards for his research, teaching, and industrial contributions.
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