3D Intracranial Aneurysm Classification and Segmentation Via Unsupervised Dual-Branch Learning.

Abstract

Intracranial aneurysms are common nowadays and how to detect them intelligently is of great significance in digital health. Whereas most existing deep learning research focused on medical images in a supervised way, we introduce an unsupervised method for the detection of intracranial aneurysms based on 3D point cloud data. In particular, our method consists of two stages: unsupervised pre-training and downstream tasks. As for the former, the main idea is to pair each point cloud with its jittering counterpart and maximise their correspondence. Then we design a dual-branch contrastive network with an encoder for each branch and a subsequent common projection head. As for the latter, we design simple networks for supervised classification and segmentation training. Experiments on the public dataset (IntrA) show that our unsupervised method achieves comparable or even better performance than some state-of-the-art supervised techniques, and it is most prominent in the detection of aneurysmal vessels. Experiments on the ModelNet-40 also show that our method achieves the accuracy of 90.79% which outperforms existing state-of-the-art unsupervised models.