Many segmentation networks have been proposed for 3D volumetric segmentation of tumors and organs at risk. Hospitals and clinical institutions seek to accelerate and minimize the efforts of specialists in image segmentation. Still, in case of errors generated by these networks, clinicians would have to manually edit the generated segmentation maps. Given a 3D volume and its putative segmentation map, we propose an approach to identify and measure erroneous regions in the segmentation map. Our method can estimate error at any point or node in a 3D mesh generated from a possibly erroneous volumetric segmentation map, serving as a Quality Assurance tool. We propose a graph neural network-based transformer based on the Nodeformer architecture to measure and classify the segmentation errors at any point. We have evaluated our network on a high-resolution micro-CT dataset of the human inner-ear bony labyrinth structure by simulating erroneous 3D segmentation maps. Our network incorporates a convolutional encoder to compute node-centric features from the input micro-CT data, the Nodeformer to learn the latent graph embeddings, and a Multi-Layer Perceptron (MLP) to compute and classify the node-wise errors. Our network achieves a mean absolute error of ~0.042 over other Graph Neural Networks (GNN) and an accuracy of 79.53% over other GNNs in estimating and classifying the node-wise errors, respectively. We also put forth vertex-normal prediction as a custom pretext task for pre-training the CNN encoder to improve the network's overall performance. Qualitative analysis shows the efficiency of our network in correctly classifying errors and reducing misclassifications.

翻译：针对肿瘤和危及器官的三维体积分割，已有许多分割网络被提出。医院和临床机构希望加速并最小化专家在图像分割中的工作量。然而，当这些网络产生错误时，临床医生仍需手动编辑生成的分割图。给定一个三维体积及其假定分割图，我们提出一种方法用于识别和测量分割图中的错误区域。我们的方法能够从潜在错误的三维体积分割图生成的网格中，对任意点或节点进行误差估计，充当质量保证工具。我们提出一种基于Nodeformer架构的图神经网络Transformer，用于在任意点测量和分类分割误差。通过模拟错误的三维分割图，我们在人耳内耳骨迷路结构的高分辨率微CT数据集上评估了该网络。我们的网络包含一个卷积编码器，用于从输入微CT数据计算以节点为中心的特征；Nodeformer用于学习潜在图嵌入；以及一个多层感知器（MLP）用于计算和分类节点级误差。与其他图神经网络（GNN）相比，我们的网络在节点级误差估计上实现了约0.042的平均绝对误差，在节点级误差分类上达到了79.53%的准确率。我们还提出将顶点法线预测作为卷积编码器预训练的自定义前置任务，以提升网络整体性能。定性分析表明，该网络在正确分类误差和减少误分类方面具有高效性。