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NA-MIC Project Weeks

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Add a MONAI Auto3DSeg inference extension to 3DSlicer

Key Investigators

Presenter location: In-person

Project Description

This project aims to implement MONAI Auto3DSeg in a 3DSlicer extension. This will enable fast inference with NVIDIA GPUs and CUDA and slower inference with CPU only. Auto3DSeg is a relatively new technique in the MONAI project and our first experiments have been successful. inference is not as complicated as using the MONAOLabel inference function.
A future aim is to integrate Auto3DSeg training into the MONAILabel extension.


  1. Objective A. Implement Auto3DSeg into a new 3D Slicer extension.

Approach and Plan

We have great starting code as well as 2 ready-to-use models from Andres Diaz-Pinto. We will build on that. In addition, we will train a lung lobe and airway model which should be available at the PW.

Progress and Next Steps

  1. Andras developed a new extension MONAI Auto3DSeg
  2. It can be downloaded via the extension manager.
  3. Andres created 3 Auto3DSeg models already to enable direct inference with CT datasets image

  4. The best models get automatically downloaded for each process
  5. They will be improved with further training
  6. In future, we attempt to enable your own training of Auto3DSeg models in MONAILabel.



Andres and Andras achieved relevant progress working on the extension during the last weeks:

The extension


(using NVIDIA RTX Geforce 3070 Ti)

We´ll continue to add relevant models.


Algorithm Generation: image

Simulate a dataset and Auto3D datalist using MONAI functions: image

Background and References