A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation
This addresses the high cost and time of manual annotation in medical imaging, offering a practical solution for researchers and clinicians, though it is incremental as it adapts active learning to 3D images.
The paper tackles the problem of expensive full annotation for 3D medical image segmentation by proposing a sparse annotation strategy using attention-guided active learning, achieving comparable results with only 15-20% annotated slices for brain extraction and 30-35% for tissue segmentation.
3D image segmentation is one of the most important and ubiquitous problems in medical image processing. It provides detailed quantitative analysis for accurate disease diagnosis, abnormal detection, and classification. Currently deep learning algorithms are widely used in medical image segmentation, most algorithms trained models with full annotated datasets. However, obtaining medical image datasets is very difficult and expensive, and full annotation of 3D medical image is a monotonous and time-consuming work. Partially labelling informative slices in 3D images will be a great relief of manual annotation. Sample selection strategies based on active learning have been proposed in the field of 2D image, but few strategies focus on 3D images. In this paper, we propose a sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation. Attention mechanism is used to improve segmentation accuracy and estimate the segmentation accuracy of each slice. The comparative experiments with three different strategies using datasets from the developing human connectome project (dHCP) show that, our strategy only needs 15% to 20% annotated slices in brain extraction task and 30% to 35% annotated slices in tissue segmentation task to achieve comparative results as full annotation.