  Accurate labeling is essential for supervised deep learning methods. However,
it is almost impossible to accurately and manually annotate thousands of
images, which results in many labeling errors for most datasets. We proposes a
local label point correction (LLPC) method to improve annotation quality for
edge detection and image segmentation tasks. Our algorithm contains three
steps: gradient-guided point correction, point interpolation and local point
smoothing. We correct the labels of object contours by moving the annotated
points to the pixel gradient peaks. This can improve the edge localization
accuracy, but it also causes unsmooth contours due to the interference of image
noise. Therefore, we design a point smoothing method based on local linear
fitting to smooth the corrected edge. To verify the effectiveness of our LLPC,
we construct a largest overlapping cervical cell edge detection dataset (CCEDD)
with higher precision label corrected by our label correction method. Our LLPC
only needs to set three parameters, but yields 30-40$\%$ average precision
improvement on multiple networks. The qualitative and quantitative experimental
results show that our LLPC can improve the quality of manual labels and the
accuracy of overlapping cell edge detection. We hope that our study will give a
strong boost to the development of the label correction for edge detection and
image segmentation. We will release the dataset and code at
https://github.com/nachifur/LLPC.
