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Lipid segmentation method based on magnification endoscopy with narrow-band imaging

WU Zhi-sheng ZOU Hong-bo ZHU Wen-wu QI Wei-ming WANG Li-qiang YUAN Bo YANG Qing XU Xiao-rong YAN Hui-hui

武治晟, 邹鸿博, 朱文武, 齐伟明, 王立强, 袁波, 杨青, 徐晓蓉, 严蕙蕙. 基于窄带成像及放大内镜的脂质分割方法[J]. 中国光学(中英文). doi: 10.37188/CO.EN-2023-0024
引用本文: 武治晟, 邹鸿博, 朱文武, 齐伟明, 王立强, 袁波, 杨青, 徐晓蓉, 严蕙蕙. 基于窄带成像及放大内镜的脂质分割方法[J]. 中国光学(中英文). doi: 10.37188/CO.EN-2023-0024
WU Zhi-sheng, ZOU Hong-bo, ZHU Wen-wu, QI Wei-ming, WANG Li-qiang, YUAN Bo, YANG Qing, XU Xiao-rong, YAN Hui-hui. Lipid segmentation method based on magnification endoscopy with narrow-band imaging[J]. Chinese Optics. doi: 10.37188/CO.EN-2023-0024
Citation: WU Zhi-sheng, ZOU Hong-bo, ZHU Wen-wu, QI Wei-ming, WANG Li-qiang, YUAN Bo, YANG Qing, XU Xiao-rong, YAN Hui-hui. Lipid segmentation method based on magnification endoscopy with narrow-band imaging[J]. Chinese Optics. doi: 10.37188/CO.EN-2023-0024

基于窄带成像及放大内镜的脂质分割方法

详细信息
  • 中图分类号: TP391.41

Lipid segmentation method based on magnification endoscopy with narrow-band imaging

doi: 10.37188/CO.EN-2023-0024
Funds: Supported by the National Natural Science Foundation of China (No. T2293751); the National Key Research and Development Program of China (No. 2021YFC2400103)
More Information
    Author Bio:

    Wu Zhi-sheng (1998—), M.Sc, College of Optical Science and Engineering, Zhejiang University. His research interests are endoscopic imaging technology and medical image processing. E-mail: 22030077@zju.edu.cn

    Qi Wei-ming (1963—), Dr, professor level senior engineer, Zhejiang Medical Device Evaluation Center. His research interests are medical device testing technology and safety evaluation. E-mail: qiweiming@zjmde.org.cn

    Wang Li-qiang (1977—), Dr, Associate Professor, Doctoral Supervisor. College of Optical Science and Engineering, Zhejiang University. His research interests are optoelectronic imaging technology and endoscopy. E-mail: wangliqiang@zju.edu.cn

    Corresponding author: qiweiming@zjmde.org.cnwangliqiang@zju.edu.cn
  • 摘要:

    一种主要成分是脂质的白色不透明物质(WOS)会覆盖与癌症诊断有关的微观结构,但WOS的形态特征又与肿瘤分级有密切关系。为了给医生提供更多与脂质相关的可用信息,本文对脂质图像的分割方法进行了研究。首先,介绍了基于Retinex框架的脂质图像增强算法,并介绍了反光去除算法。然后,介绍了基于活动轮廓模型的脂质分割方法,该方法从校正后的色调值中提取局部信息,从强度值中提取全局信息,自适应地获得权重因子,并基于初始轮廓来分割脂质区域。最后,基于自研细胞内镜成像系统,设计了仿体实验来验证了该方法的有效性。实验结果表明,该分割方法的像素准确度、灵敏度、Dice系数均高于90%。该方法能够克服照明不均匀、反光等的影响,很好地反映脂质的形状,为医生提供可用的信息。

     

  • Figure 1.  Overview of the systematically analyzing method of lipid images

    Figure 2.  The procedure of pre-processing

    Figure 3.  Lipid region segmentation results based on the LGIF model. (a) Intensity value; (b) segmentation contours (red rectangles mark incorrect segmentation areas); (c) segmentation results

    Figure 4.  (a) The pixel’s hue value; (b) the pixel’s intensity value; (c) the modified image

    Figure 5.  Experimental procedure

    Figure 6.  The prototype of the endocytoscopic imaging system. (a) The mobile workstation, including the lightbox, the video system center, and endocytoscope; (b) the knob for amplification and attitude change; (c) structure of light source; (d) the tip of the endocytoscope

    Figure 7.  The experimental subjects. (a) The phantom obtained by demoulding from the 3D-printing model; (b) the phantom covered with lipid; (c) a comparison of the reflection spectra between the phantom and pig stomach

    Figure 8.  The color, hue value, and intensity images of (a) NBI and (b)WLI

    Figure 9.  (a) The enhanced images, (b) reflective detection results, and (c) inpainting results

    Figure 10.  Enhancement and segmentation results. (a) Initial images; (b) segmentation results; (c) manual annotations

    Figure 11.  Segmentation results of triangular initial contour. (a) Initial contour; (b) incorrect segmentation results (blue lines mark the segmentation boundary)

    Figure 12.  Segmentation results using different weight factors

    Figure 13.  (a) Input images; segmentation results obtained by (b) C-V model; (c) LBF model; (d) LGIF model; (e) the proposed method; (f) manual annotations

    Figure 14.  Segmentation of WOS images in previous papers[10, 12]. (a) Initial images; (b) segmentation contours; (c) segmentation results

    Table  1.   The accuracy, sensitivity, and Dice values of the proposed method

    Test image A/% Se/% D
    Test_1 91.23 90.47 0.9029
    Test_2 93.23 91.65 0.9234
    Test_3 91.61 93.33 0.9169
    下载: 导出CSV

    Table  2.   The segmentation time and iteration numbers

    C-V LBF LGIF proposed
    iterations Time(s) iterations Time(s) iterations Time(s) iterations Time(s)
    Image1 33 2.6089 19 1.4171 18 1.3352 9 0.8359
    Image2 47 3.7690 48 3.6305 64 5.0240 6 0.6095
    Image3 27 2.5913 45 4.5998 19 1.7729 9 0.8001
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-09-04
  • 修回日期:  2023-10-20
  • 网络出版日期:  2024-03-08

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