Generalization Properties of Geometric 3D Deep Learning Models for Medical Segmentation

Recent advances in medical Deep Learning (DL) have enabled the significant reduction in time required to extract anatomical segmentations from 3-Dimensional images in an unprecedented manner. Among these methods, supervised segmentation-based approaches using variations of the UNet architecture remain extremely popular. However, these methods remain tied to the input images' resolution, and their generalisation performance relies heavily on the data distribution over the training dataset. Recently, a new family of approaches based on 3D geometric DL has emerged. These approaches encompass both implicit and explicit surface representation methods and promises to represent a 3D volume using a continuous representation of its surface whilst conserving its topological properties. It has been conjectured that these geometrical methods are more robust to out-of-distribution data and have increased generalisation properties. In this paper, we test these hypotheses for the challenging task of cortical surface reconstruction (CSR) using recently proposed architectures.