Expert-Adaptive Medical Image Segmentation
This addresses the challenge of model adaptability to different medical experts in segmentation, which is incremental as it builds on existing DNN methods with customization for expert variability.
The paper tackles the problem of deep neural networks struggling to adapt to new medical experts' annotations due to variability in expertise and tasks, proposing an expert-adaptive method that uses multi-expert annotation, multi-task training, and lightweight fine-tuning. Experiments on brain MRI segmentation with limited data show its effectiveness and parameter impact.
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.