  Deep learning techniques have shown their success in medical image
segmentation since they are easy to manipulate and robust to various types of
datasets. The commonly used loss functions in the deep segmentation task are
pixel-wise loss functions. This results in a bottleneck for these models to
achieve high precision for complicated structures in biomedical images. For
example, the predicted small blood vessels in retinal images are often
disconnected or even missed under the supervision of the pixel-wise losses.
This paper addresses this problem by introducing a long-range elastic
interaction-based training strategy. In this strategy, convolutional neural
network (CNN) learns the target region under the guidance of the elastic
interaction energy between the boundary of the predicted region and that of the
actual object. Under the supervision of the proposed loss, the boundary of the
predicted region is attracted strongly by the object boundary and tends to stay
connected. Experimental results show that our method is able to achieve
considerable improvements compared to commonly used pixel-wise loss functions
(cross entropy and dice Loss) and other recent loss functions on three retinal
vessel segmentation datasets, DRIVE, STARE and CHASEDB1.
