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NA-MIC Project Weeks

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Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR


Key Investigators

Project Description

The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segmentation in about 5 minutes that would otherwise require an average of 7h of manual work by experienced clinicians. This work focuses on the integration of the state-of-the-art UNEt TRansformers (UNETR) of the Medical Open Network for Artificial Intelligence (MONAI) framework. We trained and tested our models using 618 de-identified Cone-Beam Computed Tomography (CBCT) volumetric images of the head acquired with several parameters from different centers for a generalized clinical application. Our results on a 5-fold cross-validation showed high accuracy and robustness with an Dice up to 0.962 pm 0.02.


  1. Do some maintenance to the previously made code
  2. Train new segmentations of stable regions of reference for image registration models (Cranial Base, Mandible, Maxilla)

Approach and Plan

  1. Use the previously made code to train a model for the segmentation of the masks structures

Progress and Next Steps

  1. New segmentation models have been trained and tested
  2. An extension has been added to this module to take segmentation files as input to generate vtk files
  3. Train models to detect bone defects and patients with alveolar and palatal cleft
  4. Dicom File can be used as input


1. Different process to perform a CBCT segmentation


2. Screen of the slicer module during a segmentation

Screen slicer

3. Use of AMASSS to generate mask for a defacing tool

mask for defaceing

Background and References