  Deep learning based models, generally, require a large number of samples for
appropriate training, a requirement that is difficult to satisfy in the medical
field. This issue can usually be avoided with a proper initialization of the
weights. On the task of medical image segmentation in general, two techniques
are oftentimes employed to tackle the training of a deep network $f_T$. The
first one consists in reusing some weights of a network $f_S$ pre-trained on a
large scale database ($e.g.$ ImageNet). This procedure, also known as
$transfer$ $learning$, happens to reduce the flexibility when it comes to new
network design since $f_T$ is constrained to match some parts of $f_S$. The
second commonly used technique consists in working on image patches to benefit
from the large number of available patches. This paper brings together these
two techniques and propose to train $arbitrarily$ $designed$ $networks$ that
segment an image in one forward pass, with a focus on relatively small
databases. An experimental work have been carried out on the tasks of retinal
blood vessel segmentation and the optic disc one, using four publicly available
databases. Furthermore, three types of network are considered, going from a
very light weighted network to a densely connected one. The final results show
the efficiency of the proposed framework along with state of the art results on
all the databases.
