Perform active learning to correct problematic segmentations

sct-pipeline / fmri-segmentation

Repository for the project on automatic spinal cord segmentation based on fMRI EPI data

MIT License

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Perform active learning to correct problematic segmentations #29

Closed jcohenadad closed 6 months ago

jcohenadad commented 11 months ago

Related to #25

Procedure:

@jcohenadad finishes #25
@rohanbanerjee will re-train a model with the data labeled as ✅
@rohanbanerjee applies the new model to the data with mask labeled as ❌
@MerveKaptan and @rohanbanerjee will manually correct the masks produced by the latest model ONLY for the mask labeled as ❌
upload to the dataset
get new QC by @jcohenadad
@rohanbanerjee re-train model

rohanbanerjee commented 11 months ago

We have decided on using the hard labels and nnUNet for our task.

rohanbanerjee commented 11 months ago

We need as streamlined way to select the good flagged images from the QC and create a dataset from it. It should select those subjects and create a BIDS dataset accordingly.

Provide more details on this comment.

rohanbanerjee commented 9 months ago

The progress of this issue are as follows:

What is done

Performed by Julien in the issue: https://github.com/sct-pipeline/fmri-segmentation/issues/25
Based on the ✅ I made a separate dataset and trained a 3D nnUNet model. This dataset contained 124 subjects in total (The ✅ trained model gave a final dice score of 0.93).
After this training was completed, I ran inference on the images that were marked as ❌. The qc for these images are below.

20240129_data_excluded_qc.zip

Next steps:

Me along with @MerveKaptan will go through the qc that were marked as ❌ and work on manually correcting the images with suboptimal segmentations.
The corrected images will be QCed again
The corrected data will be added to the training data corpus and a new nnUNet model will be trained.

rohanbanerjee commented 9 months ago

Marked ✅ for the predicted segmentation which looked fine and ❌ for the subjects which had over or under-segmentations. There were a few other subjects as per me which have artifacts and I have makes them as ⚠️. The .yml files can be found below (the file named qc_fail.yml has ❌ subjects and file named qc_artifact.yml has ⚠️ subjects)

qc_inference_rb-20240129.zip. Working on correcting the segmentations which were marked as ❌ in the qc.

rohanbanerjee commented 9 months ago

Hello @jcohenadad , after a discussion with @MerveKaptan, we decided that it would be great if you QC'd these predictions. This is would maintain rater consistency and also get your inputs on which subjects we should manually correct. Below is the QC:

data_excluded_qc_all.zip

jcohenadad commented 8 months ago

overseg:

etc.

how were those predictions generated? can you point to the code, version of model, etc.

rohanbanerjee commented 8 months ago

Predictions were generated using the steps mentioned in comment

model version can be found at: fmri_sc_seg (The README contains details regarding the model version and the dataset with the ✅ marked data -- named as data_included_bids_20240113)

rohanbanerjee commented 6 months ago

Closing this issue as we are opening an issue for each round, see #38 , #35 for reference.