ESTRO 2022 - Abstract Book

S779

Abstract book

ESTRO 2022

Conclusion Cervical tumor segmentation has been performed by different 2D/3D segmentation methods. Segmentation by the 2D SegResNet model achieved the best results. Considering that our model was trained on multi-devices images, the results are promising. Our model can handle additional costs of manual contouring, variability, and poor reproducibility for the detection and segmentation of cervical cancers in T2W MR images. This study can lay solid ground for GTV detection and segmentation in LACC, which can be used for online adaptation of MRI-based RT and BT treatments. In the near future qualitative clinical validation on an independent test should be performed.

MO-0889 Validation and clinical impact of a novel hybrid cardiac substructure automatic segmentation method

V. Chin 1,2,3 , R.N. Finnegan 4,5,3 , P. Chlap 1,5,3 , J. Otton 1,6 , A. Haidar 1,5,3 , G.P. Delaney 1,2,3 , L. Holloway 1,5,4,3 , D.I. Thwaites 4 , J. Dowling 7,1,4 , S.K. Vinod 1,2,3 1 University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; 2 Ingham Institute for Applied Medical Research, Radiation Oncology, Sydney, Australia; 3 Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia; 4 University of Sydney, Institute of Medical Physics, Sydney, Australia; 5 Ingham Institute for Applied Medical Research, Medical Physics, Sydney, Australia; 6 Liverpool Hospital, Department of Cardiology, Sydney, Australia; 7 CSIRO, Australian e-Health and Research Centre, Herston, Australia

Purpose or Objective