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We propose a novel approach that reformulates anatomical landmark detection as a classification problem through a virtual agent placed inside a 3D Cone-Beam Computed Tomography (CBCT) scan. This agent is trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of Densely Connected Convolutional Networks (DCCN) and fully connected layers. Our method achieved high accuracy with an average of less than a 1.3mm error on the landmarks position without failures.
The goal is to have a model that automatically finds accurate landmarks in CBCT scans.
A virtual agent is placed inside a 3D CBCT scan. This agent is trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The decision making is processed through a deep neural network.
Selection of 6 landmark to test the method
Environment to search the agent
Architecture of the agent
The 3 steps to search the landmark
Results : (error in mm)
During this project week I learned the basics on how to develop a slicer module. I spent this week on creating a first sketch of a future module that will be used to launch the landmark prediction. For now, it allows the user to browse folders where the AI models are located and create a menu where the clinician can choose which landmark to predict. Our prediction method can be trained with any type of 3D images. This module must be user friendly and flexible so any clinician can easealy train and predict new landmarks.
Browser to load the trained models
Landmarks menu generated after reading the model folder