Title: Deep Morphing: Detecting bone structures in fluoroscopic X-ray images with prior knowledge,
Abstract: We propose approaches based on deep learning to localize objects in images
when only a small training dataset is available and the images have low
quality. That applies to many problems in medical image processing, and in
particular to the analysis of fluoroscopic (low-dose) X-ray images, where the
images have low contrast. We solve the problem by incorporating high-level
information about the objects, which could be a simple geometrical model, like
a circular outline, or a more complex statistical model. A simple geometrical
representation can sufficiently describe some objects and only requires minimal
labeling. Statistical shape models can be used to represent more complex
objects. We propose computationally efficient two-stage approaches, which we
call deep morphing, for both representations by fitting the representation to
the output of a deep segmentation network.