A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation

Understand

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.