부산대학교 도서관

In 3D medical image segmentation, data collection and annotation costs require significant human efforts. Moreover, obtaining training data is challenging due to privacy constraints. Consequently, achieving efficient learning with limited data is an urgent 3D medical image segmentation issue. One approach to address this problem is using pre-trained models, which have been widely researched. Recently, self-supervised learning for 3D medical images has gained popularity, but the data available for such learning is also scarce, limiting the number of pre-training datasets. In recent years, formula-driven supervised learning has garnered attention. It can achieve high pre-training effects using only easily accessible synthetic data, making it a promising alternative for pre-training datasets. Inspired by this approach, we propose the Auto-generated Volumetric Shapes Database (AVS-DB) for data-scarce 3D medical image segmentation tasks. AVS-DB is automatically generated from a combination of dozens of 3D models based on polygons and shape similarity ratio variations. Our experiments show that AVS-DB pre-trained models significantly outperform models trained from scratch and achieve comparable or better performance than existing self-supervised learning methods we compared. AVS-DB can potentially enhance 3D medical image segmentation models and address limited data availability challenges.