The use of deep learning methods is increasing in medical image analysis, e.g., segmentation of organs in medical images. Deep learning methods are highly dependent on a large amount of training data, a common obstacle for medical image analysis. This master thesis proposes a class-agnostic loss function as a method to train on incomplete data. The project used CT images from 1587 breast cancer patients, with a variety of available segmentation masks for each patient. The class-agnostic loss function is given labels for each class for each sample, in this project, for each segmentation mask for each CT slice. The label tells the loss function if the mask is an actual mask or just a placeholder. If it is a placeholder, the comparison of the predicted mask and the placeholder will not contribute to the loss value. The results show that it was possible to use the class-agnostic loss function to train a segmentation model with eight output masks, with data that never had all eight masks present at the same time and gain approximately the same performance as single-mask models.