  Multicolor in situ hybridization (mFISH) is a karyotyping technique used to
detect major chromosomal alterations using fluorescent probes and imaging
techniques. Manual interpretation of mFISH images is a time consuming step that
can be automated using machine learning; in previous works, pixel or patch wise
classification was employed, overlooking spatial information which can help
identify chromosomes. In this work, we propose a fully convolutional semantic
segmentation network for the interpretation of mFISH images, which uses both
spatial and spectral information to classify each pixel in an end-to-end
fashion. The semantic segmentation network developed was tested on samples
extracted from a public dataset using cross validation. Despite having no
labeling information of the image it was tested on our algorithm yielded an
average correct classification ratio (CCR) of 87.41%. Previously, this level of
accuracy was only achieved with state of the art algorithms when classifying
pixels from the same image in which the classifier has been trained. These
results provide evidence that fully convolutional semantic segmentation
networks may be employed in the computer aided diagnosis of genetic diseases
with improved performance over the current methods of image analysis.
