Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Longitudinal option / ideas for nnUNet? #2194

Open
karllandheer opened this issue May 16, 2024 · 3 comments
Open

Longitudinal option / ideas for nnUNet? #2194

karllandheer opened this issue May 16, 2024 · 3 comments
Assignees

Comments

@karllandheer
Copy link

Hello, I have used nnUNet a lot, it's a great package. I have data which is longitudinal (i.e., the same subject at two different time points). This is a fairly common use case for imaging. The question I have, is there a way to segment both images simultaneously with nnUNet? In essence, condition the segmentation of time point 2 on time point 1? I have seen other groups do this for other packages (namely brain imaging), where they claim one is able to obtain more consistent segmentations this way. Do you have any ideas on how to do that with nnUNet (beyond obviously just sequentially running it on the two time points), or any plans to incorporate this? Thanks again for your help!

@TaWald
Copy link
Contributor

TaWald commented Jun 4, 2024

Hey @karllandheer,
currently nnU-Net does not support longitudinal images, but you @mrokuss and @ykirchhoff may be able to help you out in how to use longitudinal data

@mrokuss
Copy link
Contributor

mrokuss commented Jun 5, 2024

Hey @karllandheer

We've been working on a longitudinal version of nnUNet which will hopefully be released soonish in order to exploit exactly the benefits of time series data you were describing - will keep you posted :)

@karllandheer
Copy link
Author

Wow! Great! Please keep me posted!

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

3 participants