# Introducing Geometry in Active Learning for Image Segmentation

arXiv: https://arxiv.org/abs/1508.04955 · HTML: https://mlsanity.com/paper/1508.04955/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2015.

## Built on by (most cited first, 12 of 12)

- [The Shape of Learning Curves: a Review](https://mlsanity.com/paper/2103.10948/) (2021)
- [All you need are a few pixels: semantic segmentation with PixelPick](https://mlsanity.com/paper/2104.06394/) (2021)
- [Active learning for medical image segmentation with stochastic batches](https://mlsanity.com/paper/2301.07670/) (2023)
- [Small-Text: Active Learning for Text Classification in Python](https://mlsanity.com/paper/2107.10314/) (2021)
- [Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation](https://mlsanity.com/paper/2207.00845/) (2022)
- [Full or Weak annotations? An adaptive strategy for budget-constrained annotation campaigns](https://mlsanity.com/paper/2303.11678/) (2023)
- [PyRelationAL: a python library for active learning research and development](https://mlsanity.com/paper/2205.11117/) (2022)
- [A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance](https://mlsanity.com/paper/2211.14061/) (2022)
- [A Positive/Unlabeled Approach for the Segmentation of Medical Sequences using Point-Wise Supervision](https://mlsanity.com/paper/2107.08394/) (2021)
- [Semantic Segmentation for Fully Automated Macrofouling Analysis on Coatings after Field Exposure](https://mlsanity.com/paper/2211.11607/) (2022)
- [Monotonic Learning in the PAC Framework: A New Perspective](https://mlsanity.com/paper/2501.05493/) (2025)
- [Semi-supervised Active Regression](https://mlsanity.com/paper/2106.06676/) (2021)

## Abstract (arXiv)

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.