Accurately and reliably defining organs at risk (OARs) and tumors are the cornerstone of radiation therapy (RT) treatment planning for lung cancer. Almost all segmentation networks based on deep learning techniques rely on fully annotated data with strong supervision. However, existing public imaging datasets encountered in the RT domain frequently include singly labelled tumors or partially labelled organs because annotating full OARs and tumors in CT images is both rigorous and tedious. To utilize labelled data from different sources, we proposed a dual-path semi-supervised conditional nnU-Net for OARs and tumor segmentation that is trained on a union of partially labelled datasets.
Recentl I am doing research in the direction of semi supervised segmentation. Thank you for your paper. It is very useful to me. I wonder if it's convenient for you to upload the code?
GuobinZhangTJU
commented
10 months ago
Recentl I am doing research in the direction of semi supervised segmentation. Thank you for your paper. It is very useful to me. I wonder if it's convenient for you to upload the code?