Automatic quality control of segmentation results using early epochs as data augmentation: application to choroid plexuses

The establishment of automated image segmentation methods in medical imaging allows the analysis of very large datasets. However, visual quality control (QC) of segmentation results is impractical in large datasets, hence the need for automatic QC. In this paper, we introduce a novel automatic approach for QC of segmentation results. We developed a QC deep learning model (referred to as QC model) that, for a given patient, predicts the accuracy of the corresponding automatic segmentation (in our work the Dice score) provided by a deep learning segmentation model (referred to as segmentation model) in the absence of a ground truth annotation. To train the QC model, we introduce data augmentation by using the early epochs of the segmentation model. These early epochs allow us to feed the training of the QC model with examples of poor segmentation. We applied our approach to the QC of automatic segmentation of the choroid plexuses of the brain from MRI in controls and patients with multiple sclerosis. However, the method is generic and could be used with any segmentation model. The experiments showed that the proposed approach is very effective for predicting the segmentation accuracy with a correlation coefficient of 0.92, an R² of 0.763, a mean absolute error (MAE) of 0.078, and a mean squared error (MSE) of 0.009. Overall, this work shall provide a valuable tool for the automatic QC of segmentation results.