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.
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