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

relationship between DICE and clinical metrics #30

Open
msrepo opened this issue Jun 5, 2023 · 3 comments
Open

relationship between DICE and clinical metrics #30

msrepo opened this issue Jun 5, 2023 · 3 comments
Assignees

Comments

@msrepo
Copy link
Collaborator

msrepo commented Jun 5, 2023

Since, our clinical metrics evaluation has lot of outliers (due to difficulty in automatic evaluation of these metrics), we need to filter outliers when line fitting to obtain relationship between DICE and clinical metrics, or else use L1 Regression.

Before outlier removal
image
with 90% quantile outlier removal
image

@msrepo msrepo self-assigned this Jun 5, 2023
@msrepo
Copy link
Collaborator Author

msrepo commented Jun 5, 2023

This relationship is more or less similar for other generalized metrics such as ['DSC','NSD','ASD','HD95',]
image

@msrepo
Copy link
Collaborator Author

msrepo commented Jun 5, 2023

The above figure is for a single architecture. Do this for all the architecture in a single figure.
Also, show the comparison between methods for relationship between dice and clinical metrics.

@msrepo
Copy link
Collaborator Author

msrepo commented Jun 6, 2023

i) femur head center seem to be consistently well located as dice score improves.
ii) vertebral body seem to be well reconstructed even for comparatively low dice(0.8 vs 0.9), any improvement in dice score is correlated with better reconstruction of spinous process.

The first part of statement ii) looks obvious since such a large part of the vertebra volume consists of vertebra body. Even when any peculiarities in the vertebral body such as osteophytes are not reconstructed, dice score is not affected much. For a interesting work on osteophyte reconstruction see Ambellan where region of low variability and high variability is obtained from Statistical Shape Model(SSM) to regularize the shape

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

1 participant