Automatic Cardiac Structure Contouring for Small Datasets with Cascaded Deep Learning Models.

ABSTRACT: Cardiac structure contouring is a time consuming and tedious manual activity used for radiotherapeutic dose toxicity planning. We developed an automatic cardiac structure segmentation pipeline for use in low-dose non-contrast planning CT based on deep learning algorithms for small datasets. Fifty CT scans were retrospectively selected and the whole heart, ventricles and atria were contoured. A two stage deep learning pipeline was trained on 41 non contrast planning CTs, tuned with 3 CT scans and validated on 6 CT scans. In the first stage, An InceptionResNetV2 network was used to identify the slices that contained cardiac structures. The second stage consisted of three deep learning models trained on the images containing cardiac structures to segment the structures. The three deep learnin

SUBMITTER: van den Oever LB 

PROVIDER: S-EPMC8956542 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature