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Deep learning models benefit from training with a large dataset (labeled orunlabeled). Following this motivation, we present an approach to learn a deeplearning model for the automatic segmentation of Organs at Risk (OARs) incervical cancer radiation treatment from a large clinically available datasetof Computed Tomography (CT) scans containing data inhomogeneity, label noise,and missing annotations. We employ simple heuristics for automatic datacleaning to minimize data inhomogeneity and label noise. Further, we develop asemi-supervised learning approach utilizing a teacher-student setup, annotationimputation, and uncertainty-guided training to learn in presence of missingannotations. Our experimental results show that learning from a large datasetwith our approach yields a significant improvement in the test performancedespite missing annotations in the data. Further, the contours generated fromthe segmentation masks predicted by our model are found to be equallyclinically acceptable as manually generated contours.

Clinically Acceptable Segmentation of Organs at Risk in Cervical Cancer Radiation Treatment from Clinically Available Annotations