Learning to Segment When Experts Disagree

Recent years have seen an increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depend on the quality of labels, especially in medical image domain, where both the annotation cost and inter-observer variability are high. In a typical annotation collection process, different clinical experts provide their estimates of the “true” segmentation labels under the influence of their levels of expertise and biases. Treating these noisy labels blindly as the ground truth can adversely affect the performance of supervised segmentation models. In this work, we present a neural network architecture for jointly learning, from noisy observations alone, both the reliability of individual annotators and the true segmentation label distributions. The separation of the annotators’ characteristics and true segmentation label is achieved by encouraging the estimated annotators to be maximally unreliable while achieving high fidelity with the training data. Our method can also be viewed as a translation of STAPLE, an established label aggregation framework proposed in Warfield et al. [1] to the supervised learning paradigm. We demonstrate first on a generic segmentation task using MNIST data and then adapt for usage with MRI scans of multiple sclerosis (MS) patients for lesion labelling. Our method shows considerable improvement over the relevant baselines on both datasets in terms of segmentation accuracy and estimation of annotator reliability, particularly when only a single label is available per image. An open-source implementation of our approach can be found at https://github.com/UCLBrain/MSLS.