ATMAD : robust image analysis for Automatic Tissue MicroArray De-arraying
Abstract
Background. Over the last two decades, an innovative technology called Tissue Microarray (TMA),
which combines multi-tissue and DNA microarray concepts, has been widely used in the field of
histology. It consists of a collection of several (up to 1000 or more) tissue samples that are assembled
onto a single support – typically a glass slide – according to a design grid (array) layout, in order to
allow multiplex analysis by treating numerous samples under identical and standardized conditions.
However, during the TMA manufacturing process, the sample positions can be highly distorted from
the design grid due to the imprecision when assembling tissue samples and the deformation of the
embedding waxes. Consequently, these distortions may lead to severe errors of (histological) assay
results when the sample identities are mismatched between the design and its manufactured output.
The development of a robust method for de-arraying TMA, which localizes and matches TMA
samples with their design grid, is therefore crucial to overcome the bottleneck of this prominent
technology.
Results. In this paper, we propose an Automatic, fast and robust TMA De-arraying (ATMAD)
approach dedicated to images acquired with bright field and fluorescence microscopes (or scanners).
First, tissue samples are localized in the large image by applying a locally adaptive thresholding
on the isotropic wavelet transform of the input TMA image. To reduce false detections, a parametric
shape model is considered for segmenting ellipse-shaped objects at each detected position.
Segmented objects that do not meet the size and the roundness criteria are discarded from the
list of tissue samples before being matched with the design grid. Sample matching is performed by
estimating the TMA grid deformation under the thin-plate model. Finally, thanks to the estimated
deformation, the true tissue samples that were preliminary rejected in the early image processing
step are recognized by running a second segmentation step.
Conclusions. We developed a novel de-arraying approach for TMA analysis. By combining waveletbased
detection, active contour segmentation, and thin-plate spline interpolation, our approach is
able to handle TMA images with high dynamic, poor signal-to-noise ratio, complex background and
non-linear deformation of TMA grid. In addition, the deformation estimation produces quantitative
information to asset the manufacturing quality of TMAs.
Origin : Files produced by the author(s)
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