Comments (2)
Probably for this it would be good to have a Sequence Segmentation module, which would help setting up segmentation sequence (synchronized with the input image sequence) and some tools to help with propagating segmentation between time points (e.g., using intensity based registration). This module could also have an export feature to save both input image sequence and corresponding segmentation.
This module should be probably in the Sequences extension.
from aigt.
That would be great!
from aigt.
Related Issues (20)
- The extension fails to build on all platforms HOT 2
- Add Sequence Segmentation module to Sequence extension HOT 1
- Test performance local vs OpenIGTLink vs processes
- Where is the "Processes" modules? HOT 3
- Fix Slicer extension build HOT 1
- Question about Tutorial of Deep Learning Live module
- Dataset availability HOT 2
- Unable to access dataset and trained models HOT 4
- Resizing images in prepare_data vs. resizing using Pytorch Dataset HOT 1
- Dataset __getitem__ cannot return None
- Use metics from monai and put a table of final metrics on wandb HOT 2
- Use a couple of u-net variations from monai
- Add sequence processing mode to Torch Live Ultrasound
- Add augmentation for training
- train.py stopped logging into file and prints log in terminal HOT 1
- Automatically save best model during training HOT 1
- Support transforms that are only applied on the training data HOT 2
- Support images multiple segments HOT 1
- Add option to shuffle training data HOT 1
- Record performance metrics of each trained model
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from aigt.