# Human Perception-Inspired Grain Segmentation Refinement Using Conditional Random Fields

arXiv: https://arxiv.org/abs/2312.09968 · HTML: https://mlsanity.com/paper/2312.09968/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 82 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Conditional Random Fields as Recurrent Neural Networks](https://mlsanity.com/paper/1502.03240/) (2015)
- [Boundary IoU: Improving Object-Centric Image Segmentation Evaluation](https://mlsanity.com/paper/2103.16562/) (2021)
- [Deep learning for cardiac image segmentation: A review](https://mlsanity.com/paper/1911.03723/) (2019)
- [RoadTracer: Automatic Extraction of Road Networks from Aerial Images](https://mlsanity.com/paper/1802.03680/) (2018)
- [Optimized and autonomous machine learning framework for characterizing pores, particles, grains and grain boundaries in microstructural images](https://mlsanity.com/paper/2101.06474/) (2021)
- [U-Net Based Architecture for an Improved Multiresolution Segmentation in Medical Images](https://mlsanity.com/paper/2007.08238/) (2020)
- [Contour-guided Image Completion with Perceptual Grouping](https://mlsanity.com/paper/2111.11322/) (2021)
- [Grain segmentation in atomistic simulations using orientation-based iterative self-organizing data analysis](https://mlsanity.com/paper/2112.03348/) (2021)

## Abstract (arXiv)

Automated detection of grain boundaries (GBs) in electron microscope images of polycrystalline materials could help accelerate the nanoscale characterization of myriad engineering materials and novel materials under scientific research. Accurate segmentation of interconnected line networks, such as GBs in polycrystalline material microstructures, poses a significant challenge due to the fragmented masks produced by conventional computer vision (CV) algorithms, including convolutional neural networks. These algorithms struggle with thin masks, often necessitating post-processing for effective contour closure and continuity. Previous approaches in this domain have typically relied on custom post-processing techniques that are problem-specific and heavily dependent on the quality of the mask obtained from a CV algorithm. Addressing this issue, this paper introduces a fast, high-fidelity post-processing technique that is universally applicable to segmentation masks of interconnected line networks. Leveraging domain knowledge about grain boundary connectivity, this method employs conditional random fields and perceptual grouping rules to refine segmentation masks of any image with a discernible grain structure. This approach significantly enhances segmentation mask accuracy by correctly reconstructing fragmented GBs in electron microscopy images of a polycrystalline oxide. The refinement improves the statistical representation of the microstructure, reflected by a 51 % improvement in a grain alignment metric that provides a more physically meaningful assessment of complex microstructures than conventional metrics. This method enables rapid and accurate characterization, facilitating an unprecedented level of data analysis and improving the understanding of GB networks, making it suitable for a range of disciplines where precise segmentation of interconnected line networks is essential.