Automatic Detection of Pediatric Craniofacial Deformities using Convolutional Neural Networks

Abstract

The geometric shape of our skull is very important, not only from an esthetic perspective, but also from medical viewpoint. However, the lack of designated medical experts and wrong positioning is leading to an increasing number of abnormal head shapes in newborns and infants. To make screening and therapy monitoring for these abnormal shapes easier, we develop a mobile application to automatically detect and quantify such shapes. By making use of modern machine learning technologies like deep learning and transfer learning, we have developed a convolutional neural network for semantic segmentation of bird’s-eye view images of child heads. Using this approach, we have been able to achieve a segmentation accuracy of approximately 99 %, while having sensitivity and specificity of above 98 %. Given these promising results, we will use this basis to calculate medical parameters to quantify the skull shape. In addition, we will integrate the proposed model into a mobile application for further validation and usage in a real-world scenario.

Keywords

• deep learning
• semantic image segmentation
• biomedical signals
• cranial deformities