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Many segmentation networks have been proposed for 3D volumetric segmentationof tumors and organs at risk. Hospitals and clinical institutions seek toaccelerate and minimize the efforts of specialists in image segmentation.Still, in case of errors generated by these networks, clinicians would have tomanually edit the generated segmentation maps. Given a 3D volume and itsputative segmentation map, we propose an approach to identify and measureerroneous regions in the segmentation map. Our method can estimate error at anypoint or node in a 3D mesh generated from a possibly erroneous volumetricsegmentation map, serving as a Quality Assurance tool. We propose a graphneural network-based transformer based on the Nodeformer architecture tomeasure and classify the segmentation errors at any point. We have evaluatedour network on a high-resolution micro-CT dataset of the human inner-ear bonylabyrinth structure by simulating erroneous 3D segmentation maps. Our networkincorporates a convolutional encoder to compute node-centric features from theinput micro-CT data, the Nodeformer to learn the latent graph embeddings, and aMulti-Layer Perceptron (MLP) to compute and classify the node-wise errors. Ournetwork achieves a mean absolute error of ~0.042 over other Graph NeuralNetworks (GNN) and an accuracy of 79.53% over other GNNs in estimating andclassifying the node-wise errors, respectively. We also put forth vertex-normalprediction as a custom pretext task for pre-training the CNN encoder to improvethe network's overall performance. Qualitative analysis shows the efficiency ofour network in correctly classifying errors and reducing misclassifications.

Geometric Learning-Based Transformer Network for Estimation of Segmentation Errors