Self-Supervised Masked Mesh Learning for Unsupervised Anomaly Detection on 3D Cortical Surfaces

TL;DR

This paper introduces a self-supervised masked mesh learning framework using a novel convolutional neural network to detect brain anomalies in 3D cortical surfaces, especially for Alzheimer's disease, without requiring labeled abnormal data.

Contribution

The paper presents a new self-supervised masked mesh convolutional neural network that captures normal cortical surface variation for unsupervised anomaly detection in brain imaging.

Findings

Effective detection of cortical anomalies related to Alzheimer's disease.

Utilized large-scale datasets for training and tested on clinical datasets.

Achieved accurate identification of cortical features as biomarkers.

Abstract

Unsupervised anomaly detection in brain imaging is challenging. In this paper, we propose self-supervised masked mesh learning for unsupervised anomaly detection on 3D cortical surfaces. Our framework leverages the intrinsic geometry of the cortical surface to learn a self-supervised representation that captures the underlying structure of the brain. We introduce a masked mesh convolutional neural network (MMN) that learns to predict masked regions of the cortical surface. By training the MMN on a large dataset of healthy subjects, we learn a representation that captures the normal variation in the cortical surface. We then use this representation to detect anomalies in unseen individuals by calculating anomaly scores based on the reconstruction error of the MMN. We evaluated our framework by training on population-scale dataset UKB and HCP-Aging and testing on two datasets of Alzheimer's disease patients ADNI and OASIS3. Our results show that our framework can detect anomalies in cortical thickness, cortical volume, and cortical sulcus characteristics, which are known to be biomarkers of Alzheimer's disease. Our proposed framework provides a promising approach for unsupervised anomaly detection based on normative variation of cortical features.