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3D体积分割，学术界已提出多种分割网络。医院及临床机构力求加速并减少专业人员在图像分割中的工作量。然而，当这些网络产生分割误差时，临床医生仍需手动修正生成的分割图。针对给定的3D体积及其假设分割图，我们提出一种识别并测量分割图中误差区域的方法。该方法可对由潜在错误体积分割图生成的3D网格中任意点或节点进行误差估计，可作为质量保证工具使用。我们提出一种基于Nodeformer架构的图神经网络Transformer，用于在任意节点处测量并分类分割误差。通过模拟生成含误差的3D分割图，我们在人类内耳骨迷路结构的高分辨率显微CT数据集上评估了网络性能。该网络分别采用卷积编码器从输入显微CT数据计算节点中心特征、Nodeformer学习潜在图嵌入、多层感知机（MLP）计算并分类节点级误差。相较于其他图神经网络（GNN），本网络在节点误差估计中平均绝对误差达到约0.042，在节点误差分类中准确率达79.53%。我们进一步提出将顶点法向预测作为卷积编码器预训练的定制前置任务，以提升网络整体性能。定性分析表明本网络能有效正确分类误差并减少误分类。