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Research Papers

Development of a Multimodal Colposcopy for Characterization of Cervical Intraepithelial Neoplasia

[+] Author and Article Information
Wenqi Ren

Department of Precision Machinery
and Precision Instrumentation,
University of Science and Technology of China,
Hefei 230027, China
e-mail: rwq1112@mail.ustc.edu.cn

Yingjie Qu

Department of Precision Machinery
and Precision Instrumentation,
University of Science and Technology of China,
Hefei 230027, China
e-mail: 1554046929@qq.com

Jiaojiao Pei

Department of Obstetrics and Gynecology,
Second Affiliated Hospital of Chongqing
Medical University,
Chongqing 400010, China
e-mail: 852975628@qq.com

Linlin Xiao

Department of Obstetrics and Gynecology,
Second Affiliated Hospital of Chongqing
Medical University,
Chongqing 400010, China
e-mail: xiaolinlinrachel@sina.com

Shiwu Zhang

Department of Precision Machinery and
Precision Instrumentation,
University of Science and Technology of China,
Hefei 230027, China
e-mail: swzhang@ustc.edu.cn

Shufang Chang

Department of Obstetrics and Gynecology,
Second Affiliated Hospital of Chongqing
Medical University,
Chongqing 400010, China
e-mail: shfch2005@163.com

Ronald X. Xu

Department of Precision Machinery
and Precision Instrumentation,
University of Science and Technology of China,
Hefei 230027, China;
Department of Biomedical Engineering,
The Ohio State University,
Columbus, OH 43210
e-mail: xu.202@osu.edu

1Corresponding authors.

Manuscript received July 15, 2016; final manuscript received February 12, 2017; published online June 27, 2017. Assoc. Editor: Chris Rylander.

J. Med. Devices 11(3), 031005 (Jun 27, 2017) (10 pages) Paper No: MED-16-1270; doi: 10.1115/1.4036335 History: Received July 15, 2016; Revised February 12, 2017

To develop and evaluate the clinical application of a multimodal colposcopy combining multispectral reflectance, autofluorescence, and red, green, blue (RGB) imaging for noninvasive characterization of cervical intraepithelial neoplasia (CIN). We developed a multimodal colposcopy system that combined multispectral reflectance, autofluorescence, and RGB imaging for noninvasive characterization of CIN. We studied the optical properties of cervical tissue first; then the imaging system was designed and tested in a clinical trial where comprehensive datasets were acquired and analyzed to differentiate between squamous normal and high grade types of cervical tissue. The custom-designed multimodal colposcopy is capable of acquiring multispectral reflectance images, autofluorescence images, and RGB images of cervical tissue consecutively. The classification algorithm was employed on both normal and abnormal cases for image segmentation. The performance characteristics of this system were comparable to the gold standard histopathologic measurements with statistical significance. Our pilot study demonstrated the clinical potential of this multimodal colposcopic system for noninvasive characterization of CIN. The proposed system was simple, noninvasive, cost-effective, and portable, making it a suitable device for deployment in developing countries or rural regions of limited resources.

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Figures

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Fig. 4

System diagram of the multimodal colposcopy: (a) diagram of the multimodal colposcopy system, (b) front view of the multispectral LED light source, and (c) photography of the multimodal colposcopy system

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Fig. 3

Cervical autofluorescence spectra at 365 nm excitation. The figure is modified based on Ref. 25. The shaded area shows the fluorescence intensity disparity between normal and abnormal tissues.

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Fig. 2

(a) Monte Carlo simulation and analytical model results of cervical tissue reflectance spectrum. (b) Second derivative reflectance spectrum based on MC simulated results. Three significant disparate points at 545 nm, 560 nm, and 575 nm are indicated for discrimination between normal and abnormal tissues.

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Fig. 1

(a) Geometry of the cervical tissue model. The cervical tissue is composed of two layers: epithelium and stroma. The thickness of abnormal epithelium layer is a little larger than that of normal epithelium and the stroma is thought as semi-infinite. (b) Absorption coefficients of SN and HG tissues. (c) Scattering coefficients of SN and HG tissues.

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Fig. 7

A typical raw multimodal images dataset. The top row displays the cervical images without applying any contrast agent, the second and third rows illustrate postacetic acid and post-Lugol's iodine RGB images of cervix, respectively. The lower right box indicates the preliminary process results of multispectral data.

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Fig. 8

Images of a CIN patient case: (a) RGB image, (b) green channel enhanced RGB image, (c) RGB image postacetic acid, (d) RGB image post-Lugol's iodine, (e) multispectral image after second derivative process, (f) segmentation image based on reflectance images, (g) enhanced auto fluorescent image, and (h) corrected segmentation result based on fluorescence. The pathology section codes on lower row are used to correlate the pathology diagnosis to the images. The segmentation images have three parts: background area, normal tissue (largest areas) and abnormal tissue (smallest areas), respectively.

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Fig. 5

The relative spectral power distribution of the multispectral LED light source. Among them, the 365 nm light is used for autofluorescence excitation; the 475 nm, 545 nm, and 635 nm light are used for RGB image illumination; and the rest are used for multispectral illumination.

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Fig. 6

Validation of light source illumination uniformity: (a) illumination simulation model in tracepro software, (b) the simulative illuminance distribution map on an observe plane, and (c) comparison of the central line intensity profile between simulation and experimental measurement

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Fig. 10

ROC curves for classifier discrimination of HG versus SN sample for different assessment methods. The solid line plots the results calculated from multispectral images only. The dotted line plots the results calculated by combining multispectral and fluorescence images together.

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Fig. 9

Images of a normal patient case: (a) RGB image, (b) green channel enhanced RGB image, (c) RGB image postacetic acid, (d) RGB image post-Lugol's iodine, (e) multispectral image after second derivative process, (f) segmentation image based on reflectance images, (g) enhanced auto fluorescent image, and (h) corrected segmentation result based on fluorescence. The pathology section codes on lower row are used to correlate the pathology diagnosis to the images. The segmentation images have three parts: background area, normal tissue (largest areas) and abnormal tissue (smallest areas), respectively.

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