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.
Cite
@article{arxiv.1808.04441,
title = {Deep Morphing: Detecting bone structures in fluoroscopic X-ray images with prior knowledge},
author = {Aaron Pries and Peter J. Schreier and Artur Lamm and Stefan Pede and Jürgen Schmidt},
journal= {arXiv preprint arXiv:1808.04441},
year = {2018}
}