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Semantic image segmentation is one of the most important tasks in medicalimage analysis. Most state-of-the-art deep learning methods require a largenumber of accurately annotated examples for model training. However, accurateannotation is difficult to obtain especially in medical applications. In thispaper, we propose a spatially constrained deep convolutional neural network(DCNN) to achieve smooth and robust image segmentation using inaccuratelyannotated labels for training. In our proposed method, image segmentation isformulated as a graph optimization problem that is solved by a DCNN modellearning process. The cost function to be optimized consists of a unary termthat is calculated by cross entropy measurement and a pairwise term that isbased on enforcing a local label consistency. The proposed method has beenevaluated based on corneal confocal microscopic (CCM) images for nerve fibersegmentation, where accurate annotations are extremely difficult to beobtained. Based on both the quantitative result of a synthetic dataset andqualitative assessment of a real dataset, the proposed method has achievedsuperior performance in producing high quality segmentation results even withinaccurate labels for training.

A Spatially Constrained Deep Convolutional Neural Network for Nerve  Fiber Segmentation in Corneal Confocal Microscopic Images using Inaccurate  Annotations