Annotation of 2d images with MONAILabel and 3d slicer

Is there any MONAILabel app for annotating 2d(png, jpeg) images ?

I worked on annotating 3d(nifti) images with MONAILabel and 3d slicer. It is a very nice product and i liked it.

I am exploring the annotation of 2d images with MONAILabel but i could not find the work done on the same. So, Any help about the same would be appreciated. Like links to previous works or guidance for me to achieve the same.

My current work: I have done few changes to the existing code in deepedit/main.py file like in_channles, spatial_dimentsions of UNet model. But i am facing errors related to image headers. looks like my loaded dataset is still getting considered as nifti.

[SachidanandAlle]

actually it should be very simple… for deepgrow 2D model, we train on 2D images… basically take z dimension and get all the 2D slices and train a model…

For pathology it’s all 2D… may be WSI inference is the extra thing… which we don’t have to worry in case of smaller 2D images… It’s all how you craft your pre-transforms to created the required input to train/infer a model…

Once you have the model ready as part of monailabel server app… then you can try using direct apis in http://127.0.0.1:8000/ to run basic infer/train actions… and later you can see something similar working in any clients which can support those images/label masks for rendering

[lassoan]

If in the 2D image you have origin, spacing, orientation information or the image is large (>4GB), or use bit depth >8 then I would use nrrd/nifti/DICOM, because these 3D formats can store all these data in a standard way, while consumer image file formats (png, jpg, etc.) struggle.

However, if the user just works with uncalibrated RGB images (for example photos) then it would make sense to allow MONAILabel to use that format and not require the user to convert to/from nifti.