부산대학교 도서관

Brain MRI segmentation plays an important role in analyzing brain anatomical structures and understanding brain images. In this paper, we consider building a uniform 3D brain MRI segmentation framework using the pre-training and fine- tuning style to fully leverage existing public brain images and segmentation masks. Based on existing Transformer-based 3D image segmentation models, UNETR and Swin UNETR, we study the necessity and benefit of using pre-training, through pretraining on a big collection of over 6,000 brain scans from OASIS, ADNI, and CC359, and fine-tuning with limited segmentation masks to perform three downstream tasks, i.e., skull stripping, 4-structure segmentation, and 33-structure segmentation. Experimental results demonstrate that in most cases the pre-training can help reduce 90% of segmentation masks and half the time. Also, our method outperforms the recent method SynthSeg by a good margin. Our pre-trained model and source code are available online at https://github.com/AllanIverson/medical-segmentation.