  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.
