  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.
