Deep learning-based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy.

ABSTRACT: Deep learning (DL) models for radiation therapy (RT) image segmentation require accurately annotated training data. Multiple organ delineation guidelines exist; however, information on the used guideline is not provided with the delineation. Extraction of training data with coherent guidelines can therefore be challenging. We present a supervised classification method for pelvis structure delineations where bowel cavity, femoral heads, bladder, and rectum data, with two guidelines, were classified. The impact on DL-based segmentation quality using mixed guideline training data was also demonstrated. Bowel cavity was manually delineated on CT images for anal cancer patients (n = 170) according to guidelines Devisetty and RTOG. The DL segmentation quality from using training data with cohere

SUBMITTER: Lempart M 

PROVIDER: S-EPMC10476996 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature