  Digitization techniques for biomedical images yield different visual patterns
in radiological exams. These differences may hamper the use of data-driven
approaches for inference over these images, such as Deep Neural Networks.
Another noticeable difficulty in this field is the lack of labeled data, even
though in many cases there is an abundance of unlabeled data available.
Therefore an important step in improving the generalization capabilities of
these methods is to perform Unsupervised and Semi-Supervised Domain Adaptation
between different datasets of biomedical images. In order to tackle this
problem, in this work we propose an Unsupervised and Semi-Supervised Domain
Adaptation method for segmentation of biomedical images using Generative
Adversarial Networks for Unsupervised Image Translation. We merge these
unsupervised networks with supervised deep semantic segmentation architectures
in order to create a semi-supervised method capable of learning from both
unlabeled and labeled data, whenever labeling is available. We compare our
method using several domains, datasets, segmentation tasks and traditional
baselines, such as unsupervised distance-based methods and reusing pretrained
models both with and without Fine-tuning. We perform both quantitative and
qualitative analysis of the proposed method and baselines in the distinct
scenarios considered in our experimental evaluation. The proposed method shows
consistently better results than the baselines in scarce labeled data
scenarios, achieving Jaccard values greater than 0.9 and good segmentation
quality in most tasks. Unsupervised Domain Adaptation results were observed to
be close to the Fully Supervised Domain Adaptation used in the traditional
procedure of Fine-tuning pretrained networks.
