  Partly due to the use of exhaustive-annotated data, deep networks have
achieved impressive performance on medical image segmentation. Medical imaging
data paired with noisy annotation are, however, ubiquitous, but little is known
about the effect of noisy annotation on deep learning-based medical image
segmentation. We studied the effects of noisy annotation in the context of
mandible segmentation from CT images. First, 202 images of Head and Neck cancer
patients were collected from our clinical database, where the organs-at-risk
were annotated by one of 12 planning dosimetrists. The mandibles were roughly
annotated as the planning avoiding structure. Then, mandible labels were
checked and corrected by a physician to get clean annotations. At last, by
varying the ratios of noisy labels in the training data, deep learning-based
segmentation models were trained, one for each ratio. In general, a deep
network trained with noisy labels had worse segmentation results than that
trained with clean labels, and fewer noisy labels led to better segmentation.
When using 20% or less noisy cases for training, no significant difference was
found on the prediction performance between the models trained by noisy or
clean. This study suggests that deep learning-based medical image segmentation
is robust to noisy annotations to some extent. It also highlights the
importance of labeling quality in deep learning
