Classification and analysis of human ovarian tissue using full field optical coherence tomography

Nandy, Sreyankar; Sanders, Melinda; Zhu, Quing

doi:10.1364/boe.7.005182

Cited by 30 publications

(26 citation statements)

References 28 publications

(25 reference statements)

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“…Table indicates that FracDim can be seen as a reliable indicator in four histopathological liver tissue, whereas Mean, Skewness, Kurtosis, and Entropy only perform obviously in the single microstructure. Nandy et al choose five features, including mean, variance, skewness, kurtosis, and entropy, to train a linear FF‐OCT classifier model . However, their results showed that the mean value does not contain much difference between normal and cancerous tumor.…”

Section: Resultsmentioning

confidence: 54%

“…Due to the benefits of en face tomographic images which is similarly used in biopsy, a few research groups combined FF‐OCT (full‐field optical coherence tomography) with the machine learning algorithm to obtain perfusion, motility, and biomechanics of human tumor for quantitive classification and analysis . Nevertheless, these extracted features only cover the distribution of pixel intensity values, did not reveal high‐resolution optical properties of refraction index in biological tissue, such as the spatial frequency components of the tissue scattering potential, which determines the microstructures reconstructed with FF‐OCT .…”

Section: Introductionmentioning

confidence: 95%

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Liver tissue classification of en face images by fractal dimension‐based support vector machine

Zhu

Gao

Guo

et al. 2020

Journal of Biophotonics

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Full-field optical coherence tomography (FF-OCT) has been reported with its label-free subcellular imaging performance. To realize quantitive cancer detection, the support vector machine model of classifying normal and cancerous human liver tissue is proposed with en face tomographic images. Twenty samples (10 normal and 10 cancerous) were operated from humans and composed of 285 en face tomographic images. Six histogram features and one proposed fractal dimension parameter that reveal the refractive index inhomogeneities of tissue were extracted and made up the training set. The other different 16 samples (8 normal and 8 cancerous) were imaged (190 images) and employed as the test set with the same features. First, a subcellular-resolution tomographic image library for four histopathological areas in liver tissue was established. Second, the area under the receiver operating characteristics of 0.9378, 0.9858, 0.9391, 0.9517 for prediction of the cancerous hepatic cell, central vein, fibrosis, and portal vein were measured with the test set. The results indicate that the proposed classifier from FF-OCT images shows promise as a label-free assessment of quantified tumor detection, suggesting the fractal dimension-based classifier could aid clinicians in detecting tumor boundaries for resection in surgery in the future. K E Y W O R D S en face tomographic image, fractal analysis, optical coherence tomography, support vector machine

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Section: Resultsmentioning

confidence: 54%

Section: Introductionmentioning

confidence: 95%

Liver tissue classification of en face images by fractal dimension‐based support vector machine

Zhu

Gao

Guo

et al. 2020

Journal of Biophotonics

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“…[78][79][80] Demonstrated capabilities of OCT in the detection of morphological alterations can be employed for monitoring PDT of gynecologic localizations for treatment optimization and personalization. Additional advantages in diagnostic accuracy can be provided by advanced OCT modalities, such as polarization-sensitive OCT, providing information about birefringent Ex vivo full-field OCT + image processing 58,59 Detection of ovary cancer 92% /88%…”

Section: Resultsmentioning

confidence: 99%

“…55 Recent animal studies aimed at proving the efficacy of OCT in ovary transplantation 56,57 demonstrated high potential of OCT in detecting metastases in the ovary, including micrometastases. 56 A recent ex vivo study employing full-field OCT 58,59 demonstrated that application of standard image characterization (calculation of mean, variance, skewness, kurtosis, and entropy) to OCT images of ovaries allowed to differentiate normal ovary from a malignant one with a sensitivity of 92% and specificity of 88%. It is worth mentioning that, despite quite extensive studies on OCT applications for detection of ovarian cancer, most of the works are ex vivo, and the technology is yet to be translated to clinical practice.…”

Section: Optical Coherence Tomography Diagnostics Of Ovarymentioning

confidence: 99%

Optical coherence tomography in gynecology: a narrative review

2017

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Abstract. Modern gynecologic practice requires noninvasive diagnostics techniques capable of detecting morphological and functional alterations in tissues of female reproductive organs. Optical coherence tomography (OCT) is a promising tool for providing imaging of biotissues with high resolution at depths up to 2 mm. Design of the customized probes provides wide opportunities for OCT use in gynecology. This paper contains a retrospective insight into the history of OCT employment in gynecology, an overview of the existing gynecologic OCT probes, including those for combination with other diagnostic modalities, and state-of-the-art application of OCT for diagnostics of tumor and nontumor pathologies of female genitalia. Perspectives of OCT both in diagnostics and treatment planning and monitoring in gynecology are overviewed. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…[14][15][16][17]20 A myriad of different image analysis techniques have recently been investigated in the scope of classifying tissue health based on OCT images. Some examples include structure and texture analysis, [21][22][23][24][25] convolutional neural networks, 13,26,27 and other machine-learning techniques. 10,28,29 Quantitatively characterizing tissue with such approaches has shown great promise as a diagnostic aid.…”

Section: Optical Coherence Tomographymentioning

confidence: 99%

Evaluation of segmentation algorithms for optical coherence tomography images of ovarian tissue

et al. 2019

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Ovarian cancer has the lowest survival rate among all gynecologic cancers predominantly due to late diagnosis. Early detection of ovarian cancer can increase 5-year survival rates from 40% up to 92%, yet no reliable early detection techniques exist. Optical coherence tomography (OCT) is an emerging technique that provides depth-resolved, high-resolution images of biological tissue in real-time and demonstrates great potential for imaging of ovarian tissue. Mouse models are crucial to quantitatively assess the diagnostic potential of OCT for ovarian cancer imaging; however, due to small organ size, the ovaries must first be separated from the image background using the process of segmentation. Manual segmentation is time-intensive, as OCT yields three-dimensional data. Furthermore, speckle noise complicates OCT images, frustrating many processing techniques. While much work has investigated noise-reduction and automated segmentation for retinal OCT imaging, little has considered the application to the ovaries, which exhibit higher variance and inhomogeneity than the retina. To address these challenges, we evaluate a set of algorithms to segment OCT images of mouse ovaries. We examine five preprocessing techniques and seven segmentation algorithms. While all preprocessing methods improve segmentation, Gaussian filtering is most effective, showing an improvement of 32% AE 1.2%. Of the segmentation algorithms, active contours performs best, segmenting with an accuracy of 94.8% AE 1.2% compared with manual segmentation. Even so, further optimization could lead to maximizing the performance for segmenting OCT images of the ovaries.

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Classification and analysis of human ovarian tissue using full field optical coherence tomography

Cited by 30 publications

References 28 publications

Liver tissue classification of en face images by fractal dimension‐based support vector machine

Liver tissue classification of en face images by fractal dimension‐based support vector machine

Optical coherence tomography in gynecology: a narrative review

Evaluation of segmentation algorithms for optical coherence tomography images of ovarian tissue

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