PS_NBI2k

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# Benchmarking Polyp Segmentation Methods in Narrow-Band Imaging Colonoscopy Images (Accepted for IEEE JBHI)

- ## Abstract
    > In recent years, there has been significant progress in polyp segmentation in white-light imaging (WLI) colonoscopy images, particularly with methods based on deep learning (DL). However, little attention has been paid to the reliability of these methods in narrow-band imaging (NBI) data. NBI improves visibility of blood vessels and helps physicians observe complex polyps more easily than WLI, but NBI images often include polyps with small/flat appearances, background interference, and camouflage properties, making polyp segmentation a challenging task. This paper proposes a new polyp segmentation dataset (PS-NBI2K) consisting of 2,000 NBI colonoscopy images with pixel-wise annotations, and presents benchmarking results and analyses for 24 recently reported DL-based polyp segmentation methods on PS-NBI2K. The results show that existing methods struggle to locate polyps with smaller sizes and stronger interference, and that extracting both local and global features improves performance. There is also a trade-off between effectiveness and efficiency, and most methods cannot achieve the best results in both areas simultaneously. This work highlights potential directions for designing DL-based polyp segmentation methods in NBI colonoscopy images, and the release of PS-NBI2K aims to drive further development in this field.

- ## Links to the DL methods that are compared in this paper

- ## Checkpoints of the models

The checkpoints of the models above can be downloaded via [BaiduNetDisk](https://pan.baidu.com/s/1tzHOsuKlxCMCSD_UF9qg9Q), with an extraction code of `JrzX`. For each method, we have uploaded the checkpoints of all 5 trials.

- ## Prediction maps of the compared methods

The prediction maps of the methods above can also be downloaded via [BaiduNetDisk](https://pan.baidu.com/s/1_1jsUoCVkuY4uJtx7CBN_g), with an extraction code of `Hy2z`. For each method, we have uploaded the prediction maps of all 5 trials.

- ## Evaluation of the models

For all methods, we use the same evaluation tool from [the PraNet project](https://github.com/DengPingFan/PraNet) to evaluate their performance. Please refer to the `eval` folder and carefully read the README on the github repository.

- ## Access to the PS-NBI2K dataset

The dataset can now be accessed via [OneDrive](https://1drv.ms/u/c/c9f63aa44151842d/IQAthFFBpDr2IIDJpz8AAAAAAQWcA_lBsENvzHOS01w_QyY?e=usMmXH). To prevent non-scientific use of the dataset, the dataset archive is encrypted with a password. Please send an email to cooperzhuo@proton.me to get the password.

- ## Citation
    If you want to use the database, please cite our paper
    ```
    @article{psnbi2k,
        author={Yue, Guanghui and Zhuo, Guibin and Li, Siying and Zhou, Tianwei and Du, Jingfeng and Yan, Weiqing and Hou, Jingwen and Liu, Weide and Wang, Tianfu},
        journal={IEEE Journal of Biomedical and Health Informatics}, 
        title={Benchmarking Polyp Segmentation Methods in Narrow-Band Imaging Colonoscopy Images}, 
        year={2023}
    }
    ```

PS_NBI2k

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