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Synergistic tissue counterstaining and image segmentation techniques for accurate, quantitative immunohistochemistry.

ABSTRACT: Quantitative analysis of digitized IHC-stained tissue sections is increasingly used in research studies and clinical practice. Accurate quantification of IHC staining, however, is often complicated by conventional tissue counterstains caused by the color convolution of the IHC chromogen and the counterstain. To overcome this issue, we implemented a new counterstain, Acid Blue 129, which provides homogeneous tissue background staining. Furthermore, we combined this counterstaining technique with a simple, robust, fully automated image segmentation algorithm, which takes advantage of the high degree of color separation between the 3-amino-9-ethyl-carbazole (AEC) chromogen and the Acid Blue 129 counterstain. Rigorous validation of the automated technique against manual segmentation data, using Ki-67 IHC sections from rat C6 glioma and beta-amyloid IHC sections from transgenic mice with amyloid precursor protein (APP) mutations, has shown the automated method to produce highly accurate results compared with ground truth estimates based on the manually segmented images. The synergistic combination of the novel tissue counterstaining and image segmentation techniques described in this study will allow for accurate, reproducible, and efficient quantitative IHC studies for a wide range of antibodies and tissues.

SUBMITTER: Zehntner SP

PROVIDER: S-EPMC2544616 | biostudies-other | 2008 Oct

REPOSITORIES: biostudies-other

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Project description:MOTIVATION:Study of brain images of rodent animals is the most straightforward way to understand brain functions and neural basis of physiological functions. An important step in brain image analysis is to precisely assign signal labels to specified brain regions through matching brain images to standardized brain reference atlases. However, no significant effort has been made to match different types of brain images to atlas images due to influence of artifact operation during slice preparation, relatively low resolution of images and large structural variations in individual brains. RESULTS:In this study, we develop a novel image sequence matching procedure, termed accurate and robust matching brain image sequences (ARMBIS), to match brain image sequences to established atlas image sequences. First, for a given query image sequence a scaling factor is estimated to match a reference image sequence by a curve fitting algorithm based on geometric features. Then, the texture features as well as the scale and rotation invariant shape features are extracted, and a dynamic programming-based procedure is designed to select optimal image subsequences. Finally, a hierarchical decision approach is employed to find the best matched subsequence using regional textures. Our simulation studies show that ARMBIS is effective and robust to image deformations such as linear or non-linear scaling, 2D or 3D rotations, tissue tear and tissue loss. We demonstrate the superior performance of ARMBIS on three types of brain images including magnetic resonance imaging, mCherry with 4',6-diamidino-2-phenylindole (DAPI) staining and green fluorescent protein without DAPI staining images. AVAILABILITY AND IMPLEMENTATION:The R software package is freely available at https://www.synapse.org/#!Synapse:syn18638510/wiki/591054 for Not-For-Profit Institutions. If you are a For-Profit Institution, please contact the corresponding author. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

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Synergistic tissue counterstaining and image segmentation techniques for accurate, quantitative immunohistochemistry.

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