![[Search]](/static/images/search.gif){kind=small}
![[A-Z Index]](/static/images/az.gif){kind=small}
![[Contact]](/static/images/contact.gif){kind=small}
![[University of Cambridge]](/static/images/identifier2.gif){kind=small}
![[Talks.cam]](/static/images/talkslogosmall.gif)
Shuo Wang (Digital Medicine Research Centre, Fudan University)
Tuesday 29 June 2021, 13:00-14:00
Generative model-based super resolution and quality control for cardiac segmentation
- đ¤ Speaker: Shuo Wang (Digital Medicine Research Centre, Fudan University)
- đ Date & Time: Tuesday 29 June 2021, 13:00 - 14:00
- đ Venue: Virtual (see abstract for Zoom link)
Abstract
In cardiac imaging, a high-resolution geometric representation of the heart is desired for accurate assessment of its anatomical structure and function. This is not easily available due to the limit of acquisition duration and respiratory/cardiac motion in clinical practice. Stacks of multi-slice 2D images are usually acquired in clinical routine and segmentation of these images provides a low-resolution representation of cardiac anatomy, which may contain artefacts caused by motion. Here we propose a novel latent optimisation framework that jointly performs motion correction and super resolution for cardiac image segmentations based on generative learning. Moreover, quality control of the automatic segmentation results is realised via the proposed framework.
Zoom link: https://maths-cam-ac-uk.zoom.us/j/92575403744?pwd=RHhqWC9wcUVWQi9xSzc1UE9BVGk3Zz09
Meeting ID: 925 7540 3744 Passcode: 974971
Series This talk is part of the CMIH Hub seminar series series.
Included in Lists
- All CMS events
- bld31
- Cambridge Centre for Data-Driven Discovery (C2D3)
- Cambridge talks
- Chris Davis' list
- CMIH Hub seminar series
- Interested Talks
- ndk22's list
- ob366-ai4er
- rp587
- Trust & Technology Initiative - interesting events
- Virtual (see abstract for Zoom link)
- yk449
Note: Ex-directory lists are not shown.