Uncertainty Guided Incremental Interactive Medical Image Segmentation with Sparse Variational Gaussian Process

Medical image segmentation often relies on fully automated methods, but achieving the necessary accuracy in clinical settings can be challenging. Interactive segmentation methods attempt to improve accuracy by incorporating user input in areas of inaccurate segmentation. However, these methods depend on the user’s subjective judgment, without considering the uncertainty in model predictions. To address these issues, we propose an incremental interactive medical image segmentation model guided by uncertainty. First, our proposed model combines medical image segmentation with a Gaussian process (GP), models medical image segmentation as a binary pixel classification problem based on a GP, and guides the selection of the interaction location by the magnitude of the variance value of the GP to reduce the uncertainty of the model prediction. Second, we introduce the sparse variational GP (SVGP), and we propose an incremental SVGP updating paradigm so that the SVGP can incrementally update its parameters by learning only the new interaction data without having to relearn all the old data. In addition, we propose a new induced point selection strategy and combine it with SVGP to reduce the complexity of the Gaussian process for calculating the covariance matrix. We trained and tested our model on the Medical Segmentation Decathlon’s three datasets of lung, pancreas, and colon, validating its generalization performance using the ISIC2018, CVC-ClinicDB, and KiTS19 datasets.