# CRISP: A Framework for Cryo-EM Image Segmentation and Processing with Conditional Random Field

arXiv: https://arxiv.org/abs/2502.08287 · HTML: https://mlsanity.com/paper/2502.08287/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 12 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Conditional Random Fields as Recurrent Neural Networks](https://mlsanity.com/paper/1502.03240/) (2015)
- [Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs](https://mlsanity.com/paper/1803.08207/) (2018)
- [DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM](https://mlsanity.com/paper/1605.01838/) (2016)
- [Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond](https://mlsanity.com/paper/2110.14759/) (2021)
- [Centering noisy images with application to cryo-EM](https://mlsanity.com/paper/2009.04810/) (2020)

## Built on by (most cited first, 1 of 1)

- [Reconstruction-Aware Cryo-EM Particle Picking](https://mlsanity.com/paper/2608.28838/) (2026)

## Abstract (arXiv)

Differentiating signals from the background in micrographs is a critical initial step for cryogenic electron microscopy (cryo-EM), yet it remains laborious due to low signal-to-noise ratio (SNR), the presence of contaminants and densely packed particles of varying sizes. Although image segmentation has recently been introduced to distinguish particles at the pixel level, the low SNR complicates the automated generation of accurate annotations for training supervised models. Moreover, platforms for systematically comparing different design choices in pipeline construction are lacking. Thus, a modular framework is essential to understand the advantages and limitations of this approach and drive further development. To address these challenges, we present a pipeline that automatically generates high-quality segmentation maps from cryo-EM data to serve as ground truth labels. Our modular framework enables the selection of various segmentation models and loss functions. We also integrate Conditional Random Fields (CRFs) with different solvers and feature sets to refine coarse predictions, thereby producing fine-grained segmentation. This flexibility facilitates optimal configurations tailored to cryo-EM datasets. When trained on a limited set of micrographs, our approach achieves over 90% accuracy, recall, precision, Intersection over Union (IoU), and F1-score on synthetic data. Furthermore, to demonstrate our framework's efficacy in downstream analyses, we show that the particles extracted by our pipeline produce 3D density maps with higher resolution than those generated by existing particle pickers on real experimental datasets, while achieving performance comparable to that of manually curated datasets from experts.