Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation
This work addresses uncertainty estimation for semi-supervised segmentation in medical imaging, which is an incremental improvement over existing methods.
The paper tackled the problem of uncertainty estimation in semi-supervised medical image segmentation by proposing a novel method based on inconsistency under varied misclassification costs, resulting in a model called CoraNet that demonstrated superior performance compared to state-of-the-art methods on benchmark datasets like CT pancreas and MR ACDC.
In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel method of estimating uncertainty. We observe that, when assigned different misclassification costs in a certain degree, if the segmentation result of a pixel becomes inconsistent, this pixel shows a relative uncertainty in its segmentation. Therefore, we present a new semi-supervised segmentation model, namely, conservative-radical network (CoraNet in short) based on our uncertainty estimation and separate self-training strategy. In particular, our CoraNet model consists of three major components: a conservative-radical module (CRM), a certain region segmentation network (C-SN), and an uncertain region segmentation network (UC-SN) that could be alternatively trained in an end-to-end manner. We have extensively evaluated our method on various segmentation tasks with publicly available benchmark datasets, including CT pancreas, MR endocardium, and MR multi-structures segmentation on the ACDC dataset. Compared with the current state of the art, our CoraNet has demonstrated superior performance. In addition, we have also analyzed its connection with and difference from conventional methods of uncertainty estimation in semi-supervised medical image segmentation.