  Segmentation of objects of interest is one of the central tasks in medical
image analysis, which is indispensable for quantitative analysis. When
developing machine-learning based methods for automated segmentation, manual
annotations are usually used as the ground truth toward which the models learn
to mimic. While the bulky parts of the segmentation targets are relatively easy
to label, the peripheral areas are often difficult to handle due to ambiguous
boundaries and the partial volume effect, etc., and are likely to be labeled
with uncertainty. This uncertainty in labeling may, in turn, result in
unsatisfactory performance of the trained models. In this paper, we propose
superpixel-based label softening to tackle the above issue. Generated by
unsupervised over-segmentation, each superpixel is expected to represent a
locally homogeneous area. If a superpixel intersects with the annotation
boundary, we consider a high probability of uncertain labeling within this
area. Driven by this intuition, we soften labels in this area based on signed
distances to the annotation boundary and assign probability values within [0,
1] to them, in comparison with the original "hard", binary labels of either 0
or 1. The softened labels are then used to train the segmentation models
together with the hard labels. Experimental results on a brain MRI dataset and
an optical coherence tomography dataset demonstrate that this conceptually
simple and implementation-wise easy method achieves overall superior
segmentation performances to baseline and comparison methods for both 3D and 2D
medical images.
