A Bjartell

A Heidenreich

A Jemal

Anders Bjartell

Anders Edsjö

B Zitova

C Wahlby

CM Tang

DF Gleason

DG Lowe

Giuseppe Lippolis

GT Mellinger

H Park

J Chen

J Ho

J Kononen

JI Epstein

JS Beis

JT Kwak

L Bubendorf

L He

L Seveus

Leszek Helczynski

LF Wei

MA Fischler

Niels Chr Overgaard

P Siivola

UD Braumann

Y Zhan

BMC Cancer

Abstract
          
            Background
            Prostate cancer is one of the leading causes of cancer related deaths. For diagnosis, predicting the outcome of the disease, and for assessing potential new biomarkers, pathologists and researchers routinely analyze histological samples. Morphological and molecular information may be integrated by aligning microscopic histological images in a multiplex fashion. This process is usually time-consuming and results in intra- and inter-user variability. The aim of this study is to investigate the feasibility of using modern image analysis methods for automated alignment of microscopic images from differently stained adjacent paraffin sections from prostatic tissue specimens.
          
          
            Methods
            Tissue samples, obtained from biopsy or radical prostatectomy, were sectioned and stained with either hematoxylin &amp; eosin (H&amp;E), immunohistochemistry for p63 and AMACR or Time Resolved Fluorescence (TRF) for androgen receptor (AR).
            Image pairs were aligned allowing for translation, rotation and scaling. The registration was performed automatically by first detecting landmarks in both images, using the scale invariant image transform (SIFT), followed by the well-known RANSAC protocol for finding point correspondences and finally aligned by Procrustes fit. The Registration results were evaluated using both visual and quantitative criteria as defined in the text.
          
          
            Results
            Three experiments were carried out. First, images of consecutive tissue sections stained with H&amp;E and p63/AMACR were successfully aligned in 85 of 88 cases (96.6%). The failures occurred in 3 out of 13 cores with highly aggressive cancer (Gleason score ≥ 8). Second, TRF and H&amp;E image pairs were aligned correctly in 103 out of 106 cases (97%).
            The third experiment considered the alignment of image pairs with the same staining (H&amp;E) coming from a stack of 4 sections. The success rate for alignment dropped from 93.8% in adjacent sections to 22% for sections furthest away.
          
          
            Conclusions
            The proposed method is both reliable and fast and therefore well suited for automatic segmentation and analysis of specific areas of interest, combining morphological information with protein expression data from three consecutive tissue sections. Finally, the performance of the algorithm seems to be largely unaffected by the Gleason grade of the prostate tissue samples examined, at least up to Gleason score 7.
          </jats:sec

Crossref

Automatic registration of multi-modal microscopy images for integrative analysis of prostate tissue sections

Automatic registration of multi-modal microscopy images for integrative analysis of prostate tissue sections

Abstract

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