Automated segmentation of lesions and organs at risk on [ 68 Ga]Ga-PSMA-11 PET/CT images using self-supervised learning with Swin UNETR.

Elmira YazdaniNajme Karamzadeh-ZiaratiSeyyed Saeid CheshmiMahdi SadeghiParham GeramifarHabibeh VosoughiMahmood Kazemi JahromiSaeed Reza Kheradpisheh

Published in: Cancer imaging : the official publication of the International Cancer Imaging Society (2024)

Ga]Ga-PSMA-11 PET/CT images, followed by fine-tuning on a limited set of annotated images. The model generates high-quality OARs and lesion segmentation for PSMA image analysis. The generalizable model holds potential for various clinical applications, including enhanced RLT and patient-specific internal dosimetry.

Keyphrases

pet ct
deep learning
convolutional neural network
machine learning
positron emission tomography
optical coherence tomography
air pollution
computed tomography
high throughput
risk assessment
climate change
human health