  For semantic segmentation, label probabilities are often uncalibrated as they
are typically only the by-product of a segmentation task. Intersection over
Union (IoU) and Dice score are often used as criteria for segmentation success,
while metrics related to label probabilities are not often explored. However,
probability calibration approaches have been studied, which match probability
outputs with experimentally observed errors. These approaches mainly focus on
classification tasks, but not on semantic segmentation. Thus, we propose a
learning-based calibration method that focuses on multi-label semantic
segmentation. Specifically, we adopt a convolutional neural network to predict
local temperature values for probability calibration. One advantage of our
approach is that it does not change prediction accuracy, hence allowing for
calibration as a post-processing step. Experiments on the COCO, CamVid, and
LPBA40 datasets demonstrate improved calibration performance for a range of
different metrics. We also demonstrate the good performance of our method for
multi-atlas brain segmentation from magnetic resonance images.
