Relation U-Net
It addresses the need for reliable segmentation in clinical settings by providing confidence estimates, though it is incremental as it builds on U-Net.
This paper tackles the problem of medical image segmentation by proposing Relation U-Net, a neural network that outputs segmentation maps with confidence scores, achieving better accuracy than vanilla U-Net and showing a linear correlation between confidence and segmentation accuracy on test images.
Towards clinical interpretations, this paper presents a new ''output-with-confidence'' segmentation neural network with multiple input images and multiple output segmentation maps and their pairwise relations. A confidence score of the test image without ground-truth can be estimated from the difference among the estimated relation maps. We evaluate the method based on the widely used vanilla U-Net for segmentation and our new model is named Relation U-Net which can output segmentation maps of the input images as well as an estimated confidence score of the test image without ground-truth. Experimental results on four public datasets show that Relation U-Net can not only provide better accuracy than vanilla U-Net but also estimate a confidence score which is linearly correlated to the segmentation accuracy on test images.