Variational Inference for Quantifying Inter-observer Variability in Segmentation of Anatomical Structures
This addresses the issue of ambiguous lesion or organ boundaries in medical imaging for diagnosis and treatment tasks, representing an incremental improvement by explicitly modeling annotator disagreement.
The paper tackled the problem of inter-observer variability in medical image segmentation by proposing a variational inference framework to model the distribution of plausible segmentation maps, achieving effective results on the QUBIQ brain growth MRI dataset with seven annotators.
Lesions or organ boundaries visible through medical imaging data are often ambiguous, thus resulting in significant variations in multi-reader delineations, i.e., the source of aleatoric uncertainty. In particular, quantifying the inter-observer variability of manual annotations with Magnetic Resonance (MR) Imaging data plays a crucial role in establishing a reference standard for various diagnosis and treatment tasks. Most segmentation methods, however, simply model a mapping from an image to its single segmentation map and do not take the disagreement of annotators into consideration. In order to account for inter-observer variability, without sacrificing accuracy, we propose a novel variational inference framework to model the distribution of plausible segmentation maps, given a specific MR image, which explicitly represents the multi-reader variability. Specifically, we resort to a latent vector to encode the multi-reader variability and counteract the inherent information loss in the imaging data. Then, we apply a variational autoencoder network and optimize its evidence lower bound (ELBO) to efficiently approximate the distribution of the segmentation map, given an MR image. Experimental results, carried out with the QUBIQ brain growth MRI segmentation datasets with seven annotators, demonstrate the effectiveness of our approach.