Introducing Geometry in Active Learning for Image Segmentation
This work addresses the annotation bottleneck in medical and natural image segmentation, though it appears incremental as it builds on existing active learning methods.
The paper tackles the problem of reducing annotation effort in image segmentation by introducing geometric priors into active learning, resulting in a marked performance increase compared to baselines across 3D and 2D image datasets.
We propose an Active Learning approach to training a segmentation classifier that exploits geometric priors to streamline the annotation process in 3D image volumes. To this end, we use these priors not only to select voxels most in need of annotation but to guarantee that they lie on 2D planar patch, which makes it much easier to annotate than if they were randomly distributed in the volume. A simplified version of this approach is effective in natural 2D images. We evaluated our approach on Electron Microscopy and Magnetic Resonance image volumes, as well as on natural images. Comparing our approach against several accepted baselines demonstrates a marked performance increase.