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The amplification status of human epidermal growth factor receptors 2 (HER2) genes is strongly associated
with clinical outcome in patients with breast cancer. The American Society of Clinical Oncology Tumor Marker
Guidelines Panel has recommended routine testing of HER2 status on all newly diagnosed metastatic breast cancers
since 2001. Although fluorescent in situ hybridization (FISH) technology provides superior accuracy as compared with
other approaches, current manual FISH analysis methods are somewhat subjective, tedious, and may introduce interreader
variability. The goal of this preliminary study is to develop and test a computer-aided detection (CAD) scheme to
assess HER2 status using FISH images. Forty FISH images were selected for this study from our genetic laboratory. The
CAD scheme first applies an adaptive, iterative threshold method followed by a labeling algorithm to segment cells of
possible interest. A set of classification rules is then used to identify analyzable interphase cells and discard nonanalyzable
cells due to cell overlapping and/or other image staining debris (or artifacts). The scheme then maps the
detected analyzable cells onto two other gray scale images corresponding to the red and green color of the original image
followed by application of a raster scan and labeling algorithms to separately detect the HER-2/neu ("red") and CEP17
("green") FISH signals. A simple distance based criterion is applied to detect and merge split FISH signals within each
cell. The CAD scheme computes the ratio between independent "red" and "green" FISH signals of all analyzable cells
identified on an image. If the ratio is ≥ 2.0, the FISH image is assumed to have been acquired from a HER2+ case;
otherwise, the FISH image is assumed to have been acquired from HER2- case. When we applied the CAD scheme to
the testing dataset, the average computed HER2 amplification ratios were 1.06±0.25 and 2.53±0.81 for HER2- and
HER2+ samples, respectively. The results show that the CAD scheme has the ability to automatically detect HER2 status
using FISH images. The success of CAD-guided FISH image analysis could result in a more objective, consistent, and
efficient approach in determining HER2 status of breast cancers.
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