  Obtaining annotations for 3D medical images is expensive and time-consuming,
despite its importance for automating segmentation tasks. Although multi-task
learning is considered an effective method for training segmentation models
using small amounts of annotated data, a systematic understanding of various
subtasks is still lacking. In this study, we propose a multi-task segmentation
model with a contrastive learning based subtask and compare its performance
with other multi-task models, varying the number of labeled data for training.
We further extend our model so that it can utilize unlabeled data through the
regularization branch in a semi-supervised manner. We experimentally show that
our proposed method outperforms other multi-task methods including the
state-of-the-art fully supervised model when the amount of annotated data is
limited.
