  Catheters are commonly inserted life supporting devices. X-ray images are
used to assess the position of a catheter immediately after placement as
serious complications can arise from malpositioned catheters. Previous computer
vision approaches to detect catheters on X-ray images either relied on
low-level cues that are not sufficiently robust or only capable of processing a
limited number or type of catheters. With the resurgence of deep learning,
supervised training approaches are begining to showing promising results.
However, dense annotation maps are required, and the work of a human annotator
is hard to scale. In this work, we proposed a simple way of synthesizing
catheters on X-ray images and a scale recurrent network for catheter detection.
By training on adult chest X-rays, the proposed network exhibits promising
detection results on pediatric chest/abdomen X-rays in terms of both precision
and recall.
