Classification of Craniosynostosis on 2D Asymmetry Maps Using Convolutional Neural Networks
- type:Bachelor thesis
- person in charge:
Craniosynostosis is characterized by the premature fusion of sutures in an infant skull. By projecting an asymmetry metric from the vertex positions of a 3D surface mesh onto a 2D plane, asymmetry maps can be created. We have the hypothesis that such an asymmetry map can be used to classify craniosynostosis using Convolutional Neural Networks (CNNs).