A student trained convolutional neural network competing with a commercial AI software and experts in organ at risk segmentation.

ABSTRACT: This retrospective, multi-centered study aimed to improve high-quality radiation treatment (RT) planning workflows by training and testing a Convolutional Neural Network (CNN) to perform auto segmentations of organs at risk (OAR) for prostate cancer (PCa) patients, specifically the bladder and rectum. The objective of this project was to develop a clinically applicable and robust artificial intelligence (AI) system to assist radiation oncologists in OAR segmentation. The CNN was trained using manual contours in CT-datasets from diagnostic ⁶⁸Ga-PSMA-PET/CTs by a student, then validated (n = 30, PET/CTs) and tested (n = 16, planning CTs). Further segmentations were generated by a commercial artificial intelligence (cAI) software. The ground truth were manual contours from expert r

SUBMITTER: Burkle SL 

PROVIDER: S-EPMC11522297 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature