ESTRO 2023 - Abstract Book

S550

Sunday 14 May 2023

ESTRO 2023

It is feasible to quantify intra-fraction motion using 4DMRI (reconstructed from 3DVane) acquired on an MRL and this is reduced with AC. 4DMRI acquired on treatment days could be used to quantify daily intra-fraction GTV motion and verify margin expansions. These data could potentially be used to better understand intra-fraction dose deposition and inform development of advanced motion compensation strategies for abdominal RT on the MRL. PD-0659 Are paediatric-specific neural network models needed for auto-segmentation of organs on CT images? K. Kumar 1,2 , L. McIntosh 1,2 , A. Yeo 2 , T. Kron 2 , R. Franich 1 1 RMIT University, School of Science, Melbourne, Australia; 2 Peter MacCallum Cancer Centre, Department of Physical Sciences, Melbourne, Australia Purpose or Objective Paediatric datasets are not readily available for training neural networks for auto-segmentation of organs. Paediatric CT data is also highly variable, not only in patient and anatomy, but in scan dimensions, image quality and acquisition protocols. As such, the performance of neural network algorithms such as U-NET for paediatric images is not yet known, despite being well characterised for adult images. This study aims to (i) evaluate the performance of a segmentation model trained on adult data when applied to paediatric images, (ii) quantify any improvement gained by the inclusion of paediatric data in the training images, and (iii) determine whether variations between CT scanners influence the results. Materials and Methods A total of 1000 adult and 400 Paediatric CT scans with organ contours approved by expert physicians were used for training, validating, and testing the segmentation model: a self-configuring deep learning neural network known as nnU-NET. For each organ/structure, three separate models were generated: one trained on adult data only (A-trained), one on paediatric data only (P-trained) and one trained on both adult and paediatric data (A&P-trained). Paediatric images (<18 years) were acquired on two CT scanners having different acquisition protocols (e.g. helical pitch) and reconstruction algorithms (ASIR vs SAFIRE). A model trained on one scanner set was tested on the images from the other. Test data for 80 adults and 50 paediatrics were withheld from training for evaluation purposes by comparison of Dice Similarity Coefficients relative to expert contours for a selection of head and neck, abdominal, and thoracic organs. Results The A-trained model performed poorer on paediatric test data (mean DSC 0.71 – 0.84) than on adult test data (0.83 – 0.93). See Figure 1 for a sample of 7 abdominal and thoracic organs. Poorer performance on the paediatric data is statistically significant (p <0.001). Inclusion of paediatric training data improved this to mean DSCs of 0.79 – 0.97 for the combined A&P-trained model applied to paediatric test data, but the paediatric-specific P-trained model did not have a statistically significant advantage over the A&P-trained model (p=0.265), indicating that there is no need to maintain separate segmentation models for this nn-UNet architecture (see Fig 2). The inter-scanner comparison showed equivalent performance for the two scanner image sets regardless of inclusion in the training data.