  The construction of 3D medical image datasets presents several issues,
including requiring significant financial costs in data collection and
specialized expertise for annotation, as well as strict privacy concerns for
patient confidentiality compared to natural image datasets. Therefore, it has
become a pressing issue in 3D medical image segmentation to enable
data-efficient learning with limited 3D medical data and supervision. A
promising approach is pre-training, but improving its performance in 3D medical
image segmentation is difficult due to the small size of existing 3D medical
image datasets. We thus present the Primitive Geometry Segment Pre-training
(PrimGeoSeg) method to enable the learning of 3D semantic features by
pre-training segmentation tasks using only primitive geometric objects for 3D
medical image segmentation. PrimGeoSeg performs more accurate and efficient 3D
medical image segmentation without manual data collection and annotation.
Further, experimental results show that PrimGeoSeg on SwinUNETR improves
performance over learning from scratch on BTCV, MSD (Task06), and BraTS
datasets by 3.7%, 4.4%, and 0.3%, respectively. Remarkably, the performance was
equal to or better than state-of-the-art self-supervised learning despite the
equal number of pre-training data. From experimental results, we conclude that
effective pre-training can be achieved by looking at primitive geometric
objects only. Code and dataset are available at
https://github.com/SUPER-TADORY/PrimGeoSeg.
