Deep Learning for Medical Image Segmentation with Imprecise Annotation
Binyan Hu A. K. Qin
Medical image segmentation (MIS) plays an instrumental role in medical image
analysis, where considerable efforts have been devoted to automating the
process. Currently, mainstream MIS approaches are based on deep neural networks
(DNNs) which are typically trained on a dataset that contains annotation masks
produced by doctors. However, in the medical domain, the annotation masks
generated by different doctors can inherently vary because a doctor may
unnecessarily produce precise and unique annotations to meet the goal of
diagnosis. Therefore, the DNN model trained on the data annotated by certain
doctors, often just a single doctor, could undesirably favour those doctors who
annotate the training data, leading to the unsatisfaction of a new doctor who
will use the trained model. To address this issue, this work investigates the
utilization of multi-expert annotation to enhance the adaptability of the model
to a new doctor and we conduct a pilot study on the MRI brain segmentation
task. Experimental results demonstrate that the model trained on a dataset with
multi-expert annotation can efficiently cater for a new doctor, after
lightweight fine-tuning on just a few annotations from the new doctor.