  A difficult step in the process of karyotyping is segmenting chromosomes that
touch or overlap. In an attempt to automate the process, previous studies
turned to Deep Learning methods, with some formulating the task as a semantic
segmentation problem. These models treat separate chromosome instances as
semantic classes, which we show to be problematic, since it is uncertain which
chromosome should be classed as #1 and #2. Assigning class labels based on
comparison rules, such as the shorter/longer chromosome alleviates, but does
not fully resolve the issue. Instead, we separate the chromosome instances in a
second stage, predicting the orientation of the chromosomes by the model and
use it as one of the key distinguishing factors of the chromosomes. We
demonstrate this method to be effective. Furthermore, we introduce a novel
Double-Angle representation that a neural network can use to predict the
orientation. The representation maps any direction and its reverse to the same
point. Lastly, we present a new expanded synthetic dataset, which is based on
Pommier's dataset, but addresses its issues with insufficient separation
between its training and testing sets.
