# A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation

arXiv: https://arxiv.org/abs/2003.03744 · HTML: https://mlsanity.com/paper/2003.03744/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2020.

## Built on by (most cited first, 9 of 9)

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- [Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer](https://mlsanity.com/paper/2108.00358/) (2021)
- [A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches](https://mlsanity.com/paper/2105.03148/) (2021)
- [EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks](https://mlsanity.com/paper/2102.10370/) (2021)
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- [A Survey of Semen Quality Evaluation in Microscopic Videos Using Computer Assisted Sperm Analysis](https://mlsanity.com/paper/2202.07820/) (2022)
- [EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluation](https://mlsanity.com/paper/2110.07723/) (2021)
- [Dilated Continuous Random Field for Semantic Segmentation](https://mlsanity.com/paper/2202.00162/) (2022)
- [Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer](https://mlsanity.com/paper/2205.08467/) (2022)

## Abstract (arXiv)

To assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely, "mU-Net-B3", with a dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16 based patch-level segmentation method with a novel "buffer" strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%, respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows great potential in the EM segmentation field.