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Cross-Modality Vertebrae Localization
and Labeling Using Learning-Based
Approaches
Yiqiang Zhan, Bing Jian, Dewan Maneesh and Xiang Sean Zhou
Abstract Spine is one of the major organs in human body. It consists of multiple
vertebrae and inter-vertebral discs. As the locations and labels of vertebrae provide
a vertical reference framework to different organs in the torso, they play an
important role in various neurological, orthopaedic and oncological studies. On the
other hand, however, manual localization and labeling of vertebrae is often time
consuming. Therefore, automatic vertebrae localization and labeling has drawn
signi
cant attentions in the community of medical image analysis. While some
pioneer studies aim to localize and label vertebrae using domain knowledge, more
recent studies tackle this problem via machine learning technologies. With the spirit
of
, learning-based approaches are able to extract the appearance and
geometric characteristics of vertebrae more ef
“
data-driven
”
cient and effective than hand-crafted
algorithms. More importantly, it facilitates cross-modality vertebrae localization,
i.e., a generic algorithm working on different imaging modalities. In this chapter,
we start with a review of several representative learning-based vertebrae localiza-
tion and labeling methods. The key ideas of these methods are re-visited. In order to
achieve a solution that is robust to severe diseases (e.g., scoliosis) and imaging
artifacts (e.g., metal artifacts), we propose a learning-based method with two novel
components. First, instead of treating vertebrae/discs as either repetitive compo-
nents or completely independent entities, we emulate a radiologist and use a
hierarchial strategy to learn detectors dedicated to anchor (distinctive) vertebrae,
bundle (non-distinctive) vertebrae and inter-vertebral discs, respectively. At run-
time, anchor vertebrae are detected concurrently to provide redundant and dis-
tributed appearance cues robust to local imaging artifacts. Bundle vertebrae
detectors provide candidates of vertebrae with subtle appearance differences, whose
labels are mutually determined by anchor vertebrae to gain additional robustness.
Disc locations are derived from a cloud of responses from disc detectors, which is
robust to sporadic voxel-level errors. Second, owing to the non-rigidness of spine
anatomies, we employ a local articulated model to effectively model the spatial
relations across vertebrae and discs. The local articulated model fuses appearance
