Meeting Banner
Abstract #3747

Synthetic MRI-assisted Multi-Wavelet Segmentation Framework for Organs-at-Risk Delineation on CT for Radiotherapy Planning

Reza Kalantar1, Susan Lalondrelle2, Jessica M Winfield1,3, Christina Messiou1,3, Dow-Mu Koh1,3, and Matthew D Blackledge1
1Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom, 2Gynaecological Unit, The Royal Marsden Hospital, London, United Kingdom, 3Department of Radiology, The Royal Marsden Hospital, London, United Kingdom

Radiotherapy (RT) is a cornerstone treatment for cervical cancer. Accurate delineation of organs-at-risk (OARs) is critical to prevent radiation toxicity to healthy organs surrounding the tumour. However, OARs delineation is currently performed manually by clinicians which is labour- and time-intensive. Deep-learning-based algorithms have shown potential in automating this task. This study developed and evaluated a novel framework to incorporate the superior soft-tissue contrast of MRI as well as multi-wavelet image decompositions for improved OARs segmentation on CT images. The proposed framework appears to be a promising addition to the cervical cancer treatment workflow.

This abstract and the presentation materials are available to members only; a login is required.

Join Here