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

翻译：我们提出了一种新颖的方法，通过联合学习点和像素的轮廓表示来结合图分割与稠密分割技术，从而利用每种方法的优势。这解决了典型图分割方法中的缺陷，即目标未对齐导致网络难以学习判别性顶点和轮廓特征。我们的联合学习策略能够编码丰富多样的语义特征，同时缓解了基于稠密方法中常见的轮廓稳定性问题，其中像素级目标可能导致解剖学上不合理的拓扑结构。此外，我们识别了落在轮廓边界上的正确预测受到惩罚的场景，并提出一种新型混合轮廓距离损失来解决这一问题。我们的方法在多个胸部X光数据集上进行了验证，与多种基于稠密和基于点的方法相比，在分割稳定性和准确性方面表现出显著提升。我们的源代码可在www.github.com/kitbransby/Joint_Graph_Segmentation免费获取。