Abstracting Volumetric Medical Images with Sparse Keypoints for Efficient Geometric Segmentation of Lung Fissures with a Graph CNN

Abstract

Volumetric image segmentation often relies on voxel-wise classification using 3D convolutional neural networks (CNNs). However, 3D CNNs are inefficient for detecting thin structures that make up a tiny fraction of the entire image volume. We propose a geometric deep learning framework that leverages the representation of the image as a keypoint (KP) cloud and segments it with a graph convolutional network (GCN). From the sparse point segmentations, 3D meshes of the objects are reconstructed to obtain a dense surface. The method is evaluated for the lung fissure segmentation task on two public data sets of thorax CT images and compared to the nnU-Net as the current state-of-the-art 3D CNNbased method. Our method achieves fast inference times through the sparsity of the point cloud representation while maintaining accuracy. We measure a 34× speed-up at 1.5× the nnU-Net’s error with Förstner KPs and a 6× speed-up at 1.3× error with pre-segmentation KPs.