  Existing deep learning-based approaches for histopathology image analysis
require large annotated training sets to achieve good performance; but
annotating histopathology images is slow and resource-intensive. Conditional
generative adversarial networks have been applied to generate synthetic
histopathology images to alleviate this issue, but current approaches fail to
generate clear contours for overlapped and touching nuclei. In this study, We
propose a sharpness loss regularized generative adversarial network to
synthesize realistic histopathology images. The proposed network uses
normalized nucleus distance map rather than the binary mask to encode nuclei
contour information. The proposed sharpness loss enhances the contrast of
nuclei contour pixels. The proposed method is evaluated using four image
quality metrics and segmentation results on two public datasets. Both
quantitative and qualitative results demonstrate that the proposed approach can
generate realistic histopathology images with clear nuclei contours.
