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Automated Standardized Orientation for Cone-Beam Computed Tomography (CBCT)

Key Investigators

Project Description

To develop a standardized head orientation approach for medical and dental images is crucial to improve the reliability of automated image analysis towards clinical decision-making. Manual and user-dependent head orientation is time-consuming and prone to errors. For this reason, this study aims to automatically obtain the desired standardized orientation of Cone Beam Computed Tomography scans, regardless of the patient’s positioning during the scan or any CT scanner initialization changes.

The Automated Standardized Orientation (ASO) tool presented in this work automatically identifies landmarks on 3D volumes regardless of orientation, using a deep learning landmark identification algorithm that handles images with random orientation (ALI_CBCT). ASO uses a landmark-based registration approach to automatically orient a 3D volume to a common space. The method aligns the identified landmarks to a set of reference ones. The method starts by aligning 3 randomly chosen landmarks and refines their position using an Iterative Closest Point (ICP) transform. The tool also allows user-selected landmarks for precision purposes. All the transforms computed during this process are concatenated and the final transform is applied to the CBCT volume.

To make ASO more robust, a pre-orientation algorithm has been developed. This part uses a deep learning algorithm to identify the head orientation and then rotates the volume to the desired orientation. This algorithm is currently being tested and will be implemented in the ASO module. The training has been realized with random rotations.


  1. Create a Slicer Module to use this algorithm with CBCT files
  2. Make the algorithm more robust to different head orientations
  3. Do some maintenance to the previously developed ASO module

Approach and Plan

  1. Develop in collaboration with Nathan Hutin (ASO_IOS) a Slicer Module to make ASO work for both IOS and CBCT files
  2. Implement the pre-orientation algorithm to this module
  3. Use CLI version of previously developped code to make ASO FULLY-Automated (without any input from the user)

Progress and Next Steps

  1. Slicer Module has been developed:
    • In a first step, only a SEMI-Automated version has been implemented (with scan and landmark files as inputs)
    • In a second step, a FULLY-Automated version has been developed (with ONLY scan files as inputs and ALI module running in the background)
  2. Pre-orientation algorithm, DenseNet169 from MONAI library, has been implemented in the ASO module
  3. Receive input before deploying ASO to SlicerAutomatedDentalTool Extension


Oriented Output Example


User Interface


Background and References