Abstract Scope |
Image segmentation plays a central role in quantitative analysis of material microstructures. Developing efficient and effective methods for automating the segmentation process is highly valued for materials research and manufacturing. Deep neural network methods have recently demonstrated great performance in identifying different constituents in complicated microstructure datasets. However, a considerable amount of data annotations is necessary to get effective solutions while collecting high quality annotations for materials images is difficult and time-consuming. Here, we explored two strategies that can significantly reduce the amount of annotations: (1) use few image-level annotations, (2) use pixel-level scribbles. To maintain a good segmentation performance, transfer learning, data augmentation and continuity loss function were applied. The selection of images or regions to annotate was crucial in this annotation frugal segmentation pipeline. |