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

Lippolis, Giuseppe LU ; Edsjö, Anders LU ; Helczynski, Leszek LU ; Bjartell, Anders LU and Overgaard, Niels Christian LU (2013) In BMC Cancer 13. p.408-418
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



... (More)
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 & eosin (H&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&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&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&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. (Less)
Please use this url to cite or link to this publication:
@article{cb3371d9-a6e0-48c2-a102-c25401895f61,
  abstract     = {Background<br/><br>
<br/><br>
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.<br/><br>
Methods<br/><br>
<br/><br>
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).<br/><br>
<br/><br>
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.<br/><br>
Results<br/><br>
<br/><br>
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%).<br/><br>
<br/><br>
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.<br/><br>
Conclusions<br/><br>
<br/><br>
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.},
  author       = {Lippolis, Giuseppe and Edsjö, Anders and Helczynski, Leszek and Bjartell, Anders and Overgaard, Niels Christian},
  issn         = {1471-2407},
  keyword      = {Multiplex analysis,Histological sections,Hematoxylin & Eosin,p63/AMACR,Time resolved fluorescence imaging,Image registration,Scale invariant feature transform,Prostate cancer},
  language     = {eng},
  pages        = {408--418},
  publisher    = {BioMed Central},
  series       = {BMC Cancer},
  title        = {Automatic registration of multi-modal microscopy images for integrative analysis of prostate tissue sections},
  url          = {http://dx.doi.org/10.1186/1471-2407-13-408},
  volume       = {13},
  year         = {2013},
}