Automated Classification of Fluorescent in Situ cases based on HER-2/NEU status

The evaluation of fluorescent in situ
hybridization images (FISH) is one of the most widely
used methods to determine Her-2/neu status of breast
samples, a valuable prognostic indicator. Conventional
evaluation is a difficult task since it involves manual
counting of dots in multiple images. In this paper we
present a multistage algorithm for the automated
classification of FISH images from breast carcinomas.
The algorithm focuses not only on the detection of FISH
dots but also on overall case classification. The algorithm
includes two combined stages for nuclei and dot detection
respectively. The dot detection consists of a top-hat
filtering stage followed by 3D template matching to
separate real signals from noise. Nuclei segmentation
includes a non-linearity correction step, global
thresholding and a geometric rule to distinguish between
holes within a nucleus and holes between nuclei. Finally,
the marked watershed transform is used to segment cell
nuclei with markers detected as local h-dome maxima.
Combining the two stages allows the measurement of
FISH signals ratio per cell nucleus and the collective
classification of cases as positive or negative. The system
was evaluated with receiver operating characteristic
(ROC) analysis and the results were encouraging for the
further development of this method.