3D Intracranial Aneurysm Classification and Segmentation via Unsupervised Dual-branch Learning

TL;DR

This paper presents an unsupervised dual-branch contrastive learning approach for 3D intracranial aneurysm detection and segmentation, achieving competitive results with supervised methods on public datasets.

Contribution

Introduces a novel unsupervised dual-branch contrastive network for 3D aneurysm classification and segmentation, reducing reliance on labeled data.

Findings

Achieves comparable or better performance than supervised methods on IntrA dataset.

Attains 90.79% accuracy on ModelNet40 surpassing existing unsupervised models.

Excels particularly in detecting aneurysmal vessels.

Abstract

Intracranial aneurysms are common nowadays and how to detect them intelligently is of great significance in digital health. While 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 jittered 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 ModelNet40 also show that our method achieves the accuracy of 90.79\% which outperforms existing state-of-the-art unsupervised models.