  Chest X-ray is one of the most widespread examinations of the human body. In
interventional radiology, its use is frequently associated with the need to
visualize various tube-like objects, such as puncture needles, guiding sheaths,
wires, and catheters. Detection and precise localization of these tube-like
objects in the X-ray images is, therefore, of utmost value, catalyzing the
development of accurate target-specific segmentation algorithms. Similar to the
other medical imaging tasks, the manual pixel-wise annotation of the tubes is a
resource-consuming process. In this work, we aim to alleviate the lack of the
annotated images by using artificial data. Specifically, we present an approach
for synthetic data generation of the tube-shaped objects, with a generative
adversarial network being regularized with a prior-shape constraint. Our method
eliminates the need for paired image--mask data and requires only a
weakly-labeled dataset (10--20 images) to reach the accuracy of the
fully-supervised models. We report the applicability of the approach for the
task of segmenting tubes and catheters in the X-ray images, whereas the results
should also hold for the other imaging modalities.
