Joint Dense-Point Representation for Contour-Aware Graph Segmentation

Abstract

We present a novel methodology that combines graph and dense segmentation techniques by jointly learning both point and pixel contour representations, thereby leveraging the benefits of each approach. This addresses deficiencies in typical graph segmentation methods where misaligned objectives restrict the network from learning discriminative vertex and contour features. Our joint learning strategy allows for rich and diverse semantic features to be encoded, while alleviating common contour stability issues in dense-based approaches, where pixel-level objectives can lead to anatomically implausible topologies. In addition, we identify scenarios where correct predictions that fall on the contour boundary are penalised and address this with a novel hybrid contour distance loss. Our approach is validated on several Chest X-ray datasets, demonstrating clear improvements in segmentation stability and accuracy against a variety of dense- and point-based methods. Our source code is freely available at: www.github.com/kitbransby/Joint_Graph_Segmentation

arXiv commentMICCAI 2023 pre-print

References in corpus (3)

• PadChest: A large chest x-ray image dataset with multi-label annotated reports
• Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis
• Multi-center anatomical segmentation with heterogeneous labels via landmark-based models