基于窄带成像及放大内镜的脂质分割方法

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

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

1.
浙江大学光电科学与工程学院, 浙江杭州310027
2.
浙江省医疗器械审评中心, 浙江杭州310000
3.
浙江大学医学院附属第二医院消化内科, 浙江杭州310009

详细信息

中图分类号: TP391.41
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- 被引次数: 0
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

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

doi: 10.37188/CO.EN-2023-0024

1.
College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
2.
Zhejiang Center for Medical Device Evaluation, Hangzhou 310000, China
3.
Department of Gastroenterology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China

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—), male, born in Yuanping, Shanxi Province. Master’s degree. He obtained his master’s degree from Zhejiang University in 2023. His research interests are endoscopic imaging technology and medical image processing. E-mail: 22030077@zju.edu.cn

Qi Wei-ming (1966—), male, born in Tiantai, Zhejiang Province, bachelor’s degree, professional Senior Engineer. He obtained his bachelor’s degree from Zhejiang University in 1990. Currently working at the Zhejiang Center for Medical Device Evaluation, he mainly engages in research of medical device testing technology and safety evaluation. E-mail: qiweiming@zjmde.org.cn

Wang Li-qiang (1977—), male, born in Weinan, Shaanxi Province. Associate Professor, Doctoral Supervisor, College of Optical Science and Engineering, Zhejiang University. He received his Ph.D. degree from Zhejiang University in 2004. His research interests are optoelectronic imaging technology and endoscopy. E-mail: wangliqiang@zju.edu.cn

Corresponding author: qiweiming@zjmde.org.cn; wangliqiang@zju.edu.cn

摘要

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。
- 窄带光成像 /
- 脂质 /
- 分割 /
- 活动轮廓模型 /
- 仿体
Abstract:
Magnification endoscopy with narrow-band imaging (ME-NBI) has been widely used for cancer diagnosis. However, some microstructures are rendered invisible by a white opaque substance (WOS) composed mainly of lipids. In such lesions, the morphological structure of lipids becomes another marker of tumor grade. We propose a lipid segmentation method. First, the lipid image enhancement algorithm and the specular reflection correction algorithm are introduced. Then, in the framework of the active contour model, the proposed segmentation method extracts local information from modified hue value and global information from intensity value and adaptively obtains the weight factor to segment the lipid region based on the initial contour. This method’s effectiveness is verified by a phantom experiment, which shows that it attained higher than 90% in several key measures: pixel accuracy, sensitivity, and Dice coefficient. The proposed method can accurately reflect the shape of lipids to provide available information for doctors.
- narrow-band imaging /
- lipids /
- segmentation /
- active contour model /
- phantom

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

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Figure 2. The procedure of pre-processing

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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

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

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Figure 5. Experimental procedure

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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

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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

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

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

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

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

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Figure 12. Segmentation results using different weight factors

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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

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

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

Test image	A/%	S_e/%	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

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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

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