  Semantic image segmentation is one of the most important tasks in medical
image analysis. Most state-of-the-art deep learning methods require a large
number of accurately annotated examples for model training. However, accurate
annotation is difficult to obtain especially in medical applications. In this
paper, we propose a spatially constrained deep convolutional neural network
(DCNN) to achieve smooth and robust image segmentation using inaccurately
annotated labels for training. In our proposed method, image segmentation is
formulated as a graph optimization problem that is solved by a DCNN model
learning process. The cost function to be optimized consists of a unary term
that is calculated by cross entropy measurement and a pairwise term that is
based on enforcing a local label consistency. The proposed method has been
evaluated based on corneal confocal microscopic (CCM) images for nerve fiber
segmentation, where accurate annotations are extremely difficult to be
obtained. Based on both the quantitative result of a synthetic dataset and
qualitative assessment of a real dataset, the proposed method has achieved
superior performance in producing high quality segmentation results even with
inaccurate labels for training.
