SIG Medical Ultrasound (SIG-MUS)


SIGMUS transparentIt is an exciting era for medical ultrasound.  Recent developments in deep learning (artificial intelligence) and medical robotics have started to show measurable improvement in assisting ultrasound examinations, ultrasound-guided interventions, and surgery. These complex medical procedures can incorporate multiple imaging modalities including different ultrasound modes, tracked instruments, human behaviour, and human-computer interactions.  

This SIG provides a forum for identifying, investigating, and solving key challenges and exploring future research directions for the next breakthroughs in medical ultrasound.


SIG-MUS aims to bring together the medical image computing (MIC) and computer-assisted intervention (CAI) communities to work towards the next generation of medical ultrasound imaging methods and systems. We envisage a future for clinical ultrasound that truly combines advances both in MIC and CAI, acknowledging the unique capabilities of ultrasound as an interactive anatomic and functional imaging modality that can be manipulated directly by human operators or robotic systems. This SIG also helps bridge the research and clinical ultrasound communities to design and implement new ultrasound-enabled applications that provide revolutionary healthcare benefits.


Alison Noble (President)

Technikos professor of Biomedical Engineering at the University of Oxford

Yipeng Hu (Secretary)

University College London

Stephen Aylward (Treasurer)

Kitware, Inc. 


Thomas van den Beuvel

Raddboud University


Emad Boctor

Johns Hopkins University


Saskia Camps


Gabor Fichtinger

Queens University


Alexander Grimwood

University College London


Svenja Ipsen

Fraunhofer Research Institution


Kawal Rhode

King’s College London


Wolfgang Wein 



S. Kevin Zhou

Chinese Academy of Sciences