Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images. (arXiv:2207.01623v1 [eess.IV])

Tumor segmentation is a fundamental step for radiotherapy treatment planning.
To define an accurate segmentation of the primary tumor (GTVp) of oropharyngeal
cancer patients (OPC), simultaneous assessment of different image modalities is
needed, and each image volume is explored slice-by-slice from different
orientations. Moreover, the manual fixed boundary of segmentation neglects the
spatial uncertainty known to occur in tumor delineation. This study proposes a
novel automatic deep learning (DL) model to assist radiation oncologists in a
slice-by-slice adaptive GTVp segmentation …

arxiv cancer deep learning images learning segmentation thresholding uncertainty