ESTRO 2022 - Abstract Book

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Abstract book

ESTRO 2022

Results The average accuracy across all 10 landmarks on adult CT scans was 5.6 ± 0.8, comparable to the IOV limits of our training data and resolution of the scans (Table 1). Following the adaption of our model to paediatric data using transfer learning, the average p2p error was 5.4 ± 0.5 mm across all 10 landmarks, performing equally as well as in adult data and showing similar variation in accuracy depending on the landmark location (Table 1).

Conclusion We developed a CNN model to locate facial landmarks in adult and paediatric 3D CT images. We demonstrate that a model trained with adult CT scans can be successfully adapted to locate landmarks in paediatric images without a significant loss of accuracy using transfer learning. Further work would involve using a bigger paediatric dataset, including patients with facial asymmetry to explore the ability of the model to quantify structural asymmetries in different areas of the face.