Dataset Information

A Novel Automatic Digital Algorithm that Accurately Quantifies Steatosis in NAFLD on Histopathological Whole-Slide Images.

ABSTRACT:

Background

Accurate assessment of hepatic steatosis is a key to grade disease severity in non-alcoholic fatty liver disease (NAFLD).

Methods

We developed a digital automated quantification of steatosis on whole-slide images (WSIs) of liver tissue and performed a validation study. Hematoxylin-eosin stained liver tissue slides were digitally scanned, and steatotic areas were manually annotated. We identified thresholds for size and roundness parameters by logistic regression to discriminate steatosis from surrounding liver tissue. The resulting algorithm produces a steatosis proportionate area (SPA; ratio of steatotic area to total tissue area described as percentage). The software can be implemented as a Java plug-in in FIJI, in which digital WSI can be processed automatically using the Pathomation extension.

Results

We obtained liver tissue specimens from 61 NAFLD patients and 18 controls. The area under the curve of correctly classified steatosis by the algorithm was 0.970 (95% CI 0.968-0.973), P?ConclusionsWe have developed a novel digital analysis algorithm that accurately quantifies steatosis on WSIs of liver tissue. This algorithm can be incorporated when quantification of steatosis is warranted, such as in clinical trials studying efficacy of new therapeutic interventions in NAFLD. © 2019 The Authors. Cytometry Part B: Clinical Cytometry published by Wiley Periodicals, Inc. on behalf of International Clinical Cytometry Society.

SUBMITTER: Munsterman ID

PROVIDER: S-EPMC6899563 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Publications

A Novel Automatic Digital Algorithm that Accurately Quantifies Steatosis in NAFLD on Histopathological Whole-Slide Images.

Munsterman Isabelle D ID van Erp Merijn M Weijers Gert G Bronkhorst Carolien C de Korte Chris L CL Drenth Joost P H JPH van der Laak Jeroen A W M JAWM Tjwa Eric T T L ETTL

Cytometry. Part B, Clinical cytometry 20190607 6

<h4>Background</h4>Accurate assessment of hepatic steatosis is a key to grade disease severity in non-alcoholic fatty liver disease (NAFLD).<h4>Methods</h4>We developed a digital automated quantification of steatosis on whole-slide images (WSIs) of liver tissue and performed a validation study. Hematoxylin-eosin stained liver tissue slides were digitally scanned, and steatotic areas were manually annotated. We identified thresholds for size and roundness parameters by logistic regression to disc ...[more]

PMID: 31173462

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Project description:BackgroundAccurate and precise alignment of histopathology tissue sections is a key step for the interpretation of the proteome topology and cell level three-dimensional (3D) reconstruction of diseased tissues. However, the realization of an automated and robust method for aligning nonglobally stained immunohistochemical (IHC) sections is still challenging. In this study, we aim to assess the feasibility of multidimensional graph-based image registration on aligning serial-section and whole-slide IHC section images.Materials and methodsAn automated, patch graph-based registration method was established and applied to align serial, whole-slide IHC sections at ×10 magnification (average 32,947 × 27,054 pixels). The alignment began with the initial alignment of high-resolution reference and translated images (object segmentation and rigid registration) and nonlinear registration of low-resolution reference and translated images, followed by the multidimensional graph-based image registration of the segmented patches, and finally, the fusion of deformed patches for inspection. The performance of the proposed method was formulated and evaluated by the Hausdorff distance between continuous image slices.ResultsSets of average 315 patches from five serial whole slide, IHC section images were tested using 21 different IHC antibodies across five different tissue types (skin, breast, stomach, prostate, and soft tissue). The proposed method was successfully automated to align most of the images. The average Hausdorff distance was 48.93 μm with a standard deviation of 14.94 μm, showing a significant improvement from the previously published patch-based nonlinear image registration method (average Hausdorff distance of 93.89 μm with 50.85 μm standard deviation).ConclusionsOur method was effective in aligning whole-slide tissue sections at the cell-level resolution. Further advancements in the screening of the proteome topology and 3D tissue reconstruction could be expected.

Dataset Information

A Novel Automatic Digital Algorithm that Accurately Quantifies Steatosis in NAFLD on Histopathological Whole-Slide Images.

Background

Methods

Results

Publications

A Novel Automatic Digital Algorithm that Accurately Quantifies Steatosis in NAFLD on Histopathological Whole-Slide Images.

Similar Datasets

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