Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification

Stanciu, Stefan G.; Xu, Shuoyu; Qian, Peng; Yan, Jie; Welsch, Roy E.; So, Peter T. C.; Csúcs, Gábor; Yu, Hanry

doi:10.1038/srep04636

Cited by 59 publications

(43 citation statements)

References 67 publications

(87 reference statements)

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“…The SHG image at the surface of healthy pig liver tissue is dominated by signals from the Glisson's capsule; a network of fibrous connective tissue surrounding the liver . Within the sub‐capsule region of the liver this network extends into a sheathing for the blood vessels (veins and arteries) that interconnect the portal areas of the hepatic lobules.…”

Section: Resultsmentioning

confidence: 99%

Hepatic Vitamin A Content Investigation Using Coherent Anti‐Stokes Raman Scattering Microscopy

et al. 2016

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Standard techniques for examining the distribution of vitamin A in liver either require staining or lead to rapid photobleaching of the molecule. A potentially better alternative approach is to use coherent anti-Stokes Raman scattering (CARS) microscopy; a fast, label-free, non-disruptive imaging method that provides contrast based on molecular vibrations. This contribution evaluates the viability of CARS microscopy for imaging vitamin A within thick hepatic tissue under physiological conditions by tuning into its characteristic vibrational band in the fingerprint region. Additional information about the morphology and architecture of the tissue was acquired using second harmonic generation (SHG) and multi-photon excited fluorescence (MPEF) to help mapping the intra-lobular positions of the vitamin A droplets. We demonstrate the capability of our multimodal imaging framework to selectively image lipid-soluble vitamin A droplets deep in bulk liver tissue with a high contrast while co-registering a complementary morphological background that clearly visualizes hepatic lobules. The results obtained envisage the good prospect of the technique for in vivo studies assessing vitamin A distribution heterogeneity and how it is affected by the progression of hepatic diseases.

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Section: Resultsmentioning

confidence: 99%

Hepatic Vitamin A Content Investigation Using Coherent Anti‐Stokes Raman Scattering Microscopy

et al. 2016

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“…Hence, it would be beneficial to further investigate the sub‐capsular collagen and cellular information beneath the Glisson's capsule to improve the sensitivity and specificity of the capsule index for the detection of liver fibrosis at different stages. We have previously demonstrated the use of scale‐invariant feature transform (SIFT) for staging liver diseases from sub‐capsular TPEF images which can be potentially used together with the Capsule index method proposed in this study under the statistical framework of supervised learning from multiple classifiers such as proposed in .…”

Section: Resultsmentioning

confidence: 99%

Quantification of liver fibrosis via second harmonic imaging of the Glisson's capsule from liver surface

Kang

Gou

et al. 2015

Journal of Biophotonics

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Liver surface is covered by a collagenous layer called the Glisson’s capsule. The structure of the Glisson’s capsule is barely seen in the biopsy samples for histology assessment, thus the changes of the collagen network from the Glisson’s capsule during the liver disease progression are not well studied. In this report, we investigated whether non-linear optical imaging of the Glisson’s capsule at liver surface would yield sufficient information to allow quantitative staging of liver fibrosis. In contrast to conventional tissue sections whereby tissues are cut perpendicular to the liver surface and interior information from the liver biopsy samples were used, we have established a capsule index based on significant parameters extracted from the second harmonic generation (SHG) microscopy images of capsule collagen from anterior surface of rat livers. Thioacetamide (TAA) induced liver fibrosis animal models was used in this study. The capsule index is capable of differentiating different fibrosis stages, with area under receiver operating characteristics curve (AUC) up to 0.91, making it possible to quantitatively stage liver fibrosis via liver surface imaging potentially with endomicroscopy.

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“…Several texture analysis techniques have been proposed, which can be categorized into model-based, statistical and geometrical methods. Several studies have been devoted a wide variety of approaches to feature extraction (Al-Kadi, 2010;Amin & Mahmoud-Ghoneim, 2011;Bibicu, Moldovanu, Moraru, & Nicolae, 2011;Iacoviello, 2011;Mostaço-Guidolin et al 2013;Qiao & Chen, 2011;Stanciu et al, 2014). In microscopic images, the image texture is strongly influenced by the nature of the texture surface, illumination, the acquisition device, the viewing position, and by high density of lines.…”

Section: Introductionmentioning

confidence: 99%

Texture analysis of parasitological liver fibrosis images

Moraru

Moldovanu

Culea-Florescu

et al. 2017

Microscopy Res & Technique

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Liver fibrosis accurate staging is vital to define the state of the Schistosomiasis disease for further treatment. The present work analyzed the microscopic liver images to identify and to differentiate between healthy, cellular, fibrocellular, and fibrous liver pathologies by proposing a fast, robust, and highly discriminative method based on texture analysis. The multiclass classification based on the "one-versus- all" method that built a voting rule approach to classify the liver images based on the liver state. Specifically, quantitative parameters, such as the anisotropy and laminarity are proposed based on the relative orientation of the pixel pairs in a global and local coherence of gradient vectors approach. Analysis of the tissue texture data using both gradient vector and gradient angle co-occurrence matrix approaches facilitated more definitive identification of the abnormal tissue. The experimental results established that the local anisotropy based texture measures are appropriated for the microtexture analysis in order to discriminate between pathologies. Macrotexture description using the global features provided only integral anisotropy coefficient that has a confidence level similar to those provided by the local feature.

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Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification

Cited by 59 publications

References 67 publications

Hepatic Vitamin A Content Investigation Using Coherent Anti‐Stokes Raman Scattering Microscopy

Hepatic Vitamin A Content Investigation Using Coherent Anti‐Stokes Raman Scattering Microscopy

Quantification of liver fibrosis via second harmonic imaging of the Glisson's capsule from liver surface

Texture analysis of parasitological liver fibrosis images

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