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.

医学图像分割 (MIS) 在医学图像分析中起着关键作用，目前主流的 MIS 方法基于深度神经网络 (DNNs)，该方法主要训练医生生成的注释掩膜数据集，然而由于不同医生生成的注释掩膜可能存在差异，因此本文利用多专家注释来提高模型对新医生的适应性，并在 MRI 脑分割任务上进行了试验，结果表明，在只使用新医生提供的几个注释进行轻量级微调后，模型在新医生上的适用性可以得到有效增强。