In recent years, a considerable amount of methods have been proposed for detecting and reconstructing the spine
and the vertebrae from CT and MR scans. The results are either used for examining the vertebrae or serve as a
preprocessing step for further detection and annotation tasks. In this paper, we propose a method for reliably
detecting the position of the vertebrae on a single slice of a transversal body CT scan. Thus, our method is not
restricted by the available portion of the 3D scan, but even suffices with a single 2D image. A further advantage
of our method is that detection does not require adjusting parameters or direct user interaction. Technically, our
method is based on an imaging pipeline comprising five steps: The input image is preprocessed. The relevant
region of the image is extracted. Then, a set of candidate locations is selected based on bone density. In the
next step, image features are extracted from the surrounding of the candidate locations and an instance-based
learning approach is used for selecting the best candidate. Finally, a refinement step optimizes the best candidate
region. Our proposed method is validated on a large diverse data set of more than 8 000 images and improves the
accuracy in terms of area overlap and distance from the true position significantly compared to the only other
method being proposed for this task so far.
Knowledge about the vertebrae is a valuable source of information for several annotation tasks. In recent years,
the research community spent a considerable effort for detecting, segmenting and analyzing the vertebrae and
the spine in various image modalities like CT or MR. Most of these methods rely on prior knowledge like the
location of the vertebrae or other initial information like the manual detection of the spine. Furthermore, the
majority of these methods require a complete volume scan. With the existence of use cases where only a single
slice is available, there arises a demand for methods allowing the detection of the vertebrae in 2D images. In
this paper, we propose a fully automatic and parameterless algorithm for detecting the vertebrae in 2D CT
images. Our algorithm starts with detecting candidate locations by taking the density of bone-like structures
into account. Afterwards, the candidate locations are extended into candidate regions for which certain image
features are extracted. The resulting feature vectors are compared to a sample set of previously annotated and
processed images in order to determine the best candidate region. In a final step, the result region is readjusted
until convergence to a locally optimal position. Our new method is validated on a real world data set of more
than 9 329 images of 34 patients being annotated by a clinician in order to provide a realistic ground truth.
Automatically determining the relative position of a single CT slice within a full body scan provides several useful
functionalities. For example, it is possible to validate DICOM meta-data information. Furthermore, knowing
the relative position in a scan allows the efficient retrieval of similar slices from the same body region in other
volume scans. Finally, the relative position is often an important information for a non-expert user having only
access to a single CT slice of a scan. In this paper, we determine the relative position of single CT slices via
instance-based regression without using any meta data. Each slice of a volume set is represented by several
types of feature information that is computed from a sequence of image conversions and edge detection routines
on rectangular subregions of the slices. Our new method is independent from the settings of the CT scanner
and provides an average localization error of less than 4.5 cm using leave-one-out validation on a dataset of 34
annotated volume scans. Thus, we demonstrate that instance-based regression is a suitable tool for mapping
single slices to a standardized coordinate system and that our algorithm is competitive to other volume-based
approaches with respect to runtime and prediction quality, even though only a fraction of the input information
is required in comparison to other approaches.
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