  Medical image segmentation has played an important role in medical analysis
and widely developed for many clinical applications. Deep learning-based
approaches have achieved high performance in semantic segmentation but they are
limited to pixel-wise setting and imbalanced classes data problem. In this
paper, we tackle those limitations by developing a new deep learning-based
model which takes into account both higher feature level i.e. region inside
contour, intermediate feature level i.e. offset curves around the contour and
lower feature level i.e. contour. Our proposed Offset Curves (OsC) loss
consists of three main fitting terms. The first fitting term focuses on
pixel-wise level segmentation whereas the second fitting term acts as attention
model which pays attention to the area around the boundaries (offset curves).
The third terms plays a role as regularization term which takes the length of
boundaries into account. We evaluate our proposed OsC loss on both 2D network
and 3D network. Two common medical datasets, i.e. retina DRIVE and brain tumor
BRATS 2018 datasets are used to benchmark our proposed loss performance. The
experiments have shown that our proposed OsC loss function outperforms other
mainstream loss functions such as Cross-Entropy, Dice, Focal on the most common
segmentation networks Unet, FCN.
