Medical image segmentation (MIS) plays an instrumental role in medical image analysis, where considerable effort has been devoted to automating the process. Currently, mainstream MIS approaches are based on deep neural networks (DNNs), which are typically trained on a dataset with annotations produced by certain medical experts. In the medical domain, the annotations generated by different experts can be inherently distinct due to complexity of medical images and variations in expertise and post-segmentation missions. Consequently, the DNN model trained on the data annotated by some experts may hardly adapt to a new expert. In this work, we evaluate a customised expert-adaptive method, characterised by multi-expert annotation, multi-task DNN-based model training, and lightweight model fine-tuning, to investigate model's adaptivity to a new expert in the situation where the amount and mobility of training images are limited. Experiments conducted on brain MRI segmentation tasks with limited training data demonstrate its effectiveness and the impact of its key parameters.

翻译：医学图像分割在医学图像分析中起着关键作用，众多研究致力于实现该过程的自动化。当前主流医学图像分割方法基于深度神经网络，这类网络通常使用由特定医学专家标注的数据集进行训练。在医学领域，由于医学图像的复杂性、专家专业水平的差异以及分割后任务的不同，不同专家生成的标注在本质上可能存在差异。因此，基于某些专家标注数据训练的深度神经网络模型难以适应新专家。本研究评估了一种定制的专家自适应方法，该方法融合多专家标注、多任务深度神经网络模型训练及轻量级模型微调技术，旨在探究在训练图像数量有限且动态调整受限的情况下，模型对新专家的适应能力。在训练数据有限的脑部MRI分割任务上进行的实验验证了该方法的有效性及其关键参数的影响。