Segmentation Ground truth

[Muhammad_Maab_Nawaz]

I am working on a project to segment a specific part of the brain in 3D using a large dataset of CT scans. However, labeling ground truth for training is challenging since manually annotating every slice (1500+ per patient across 300 DICOMs) is not feasible. What would be a more practical approach to obtain 3D ground truth labels for these CT scans so that I can effectively train my 3D model?

[TMosh]

The classic method is to label a few examples and then use data augmentation to create a lot of variations of that example.

[Muhammad_Maab_Nawaz]

I dont have data problem.I have alot of CT scans. The problem is that I have 3d data(dicoms).I need it for segmentation task. So I also need the ground truth in 3d form. labelling each layer will take a lot of time. So I wanted to know, how do researchers do it.

[TMosh]

I believe they hire a lot of grad students to label the data for them.

[Muhammad_Maab_Nawaz]

I myself am in my undergrad.Could there be anything else than hiring.

[TMosh]

Sorry, I do not know.

Hopefully someone with experience in creating a new dataset will reply here.

[Deepti_Prasad]

hi @Muhammad_Maab_Nawaz

I have provided two links in the below comment thread and also discussion about dicom data annotation. see if it helps you

DICOM in DEEP LEARING

hi @Smoooth Go through the below link the one mentor mentioning about AI and healthcare model about CT scan image cannot be shared here as those assignments are part of graded assignment and learners who have purchased or completed AI for medicine specialisation can only access to those assignments. i had done a spinal degenerative classification model which uses CT scan images, which we then format the data based on specificity or sensitivity of features which classifies the degenerative…

Regards
DP

[Muhammad_Maab_Nawaz]

Thanks.they were helpful