A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model

Journal article


Publication Details

Author(s): Zhong X, Strobel N, Birkhold A, Kowarschik M, Fahrig R, Maier A
Journal: International Journal of Computer Assisted Radiology and Surgery
Publication year: 2019
Volume: 14
Journal issue: 1
Pages range: 53--61
ISSN: 1861-6410


Abstract

Purpose
With the recent introduction of fully assisting scanner technologies by Siemens
Healthineers (Erlangen, Germany), a patient surface model was introduced to the
diagnostic imaging device market. Such a patient representation can be used to automate
and accelerate the clinical imaging workflow, manage patient dose, and provide
navigation assistance for computed tomography diagnostic imaging. In addition
to diagnostic imaging, a patient surface model has also tremendous potential to
simplify interventional imaging. For example, if the anatomy of a patient was
known, a robotic angiography system could be automatically positioned such that
the organ of interest is positioned in the system’s iso-center offering a good
and flexible view on the underlying patient anatomy quickly and without any
additional X-ray dose. Method To enable such functionality in a clinical
context with sufficiently high accuracy, we present an extension of our
previous patient surface model by adding internal anatomical landmarks
associated with certain (main) bones of the human skeleton, in particular the
spine. We also investigate different approaches to positioning of these
landmarks employing CT datasets with annotated internal landmarks as training
data. The general pipeline of our proposed method comprises the following
steps: First, we train an active shape model using an existing avatar database
and segmented CT surfaces. This stage also includes a gravity correction
procedure, which accounts for shape changes due to the fact that the avatar
models were obtained in standing position, while the CT data were acquired with
patients in supine position. Second, we match the gravity-corrected avatar
patient surface models to surfaces segmented from the CT datasets. In the last
step, we derive the spatial relationships between the patient surface model and
internal anatomical landmarks. Result We trained and evaluated our method using
cross-validation using 20 datasets, each containing 50 internal landmarks. We
further compared the performance of four different generalized linear models’
setups to describe the positioning of the internal landmarks relative to the
patient surface. The best mean estimation error over all the landmarks was
achieved using lasso regression with a mean error of 12.19 ± 6.98 mm.
Conclusion Considering that interventional X-ray imaging systems can have
detectors covering an area of about 200 mm × 266 mm (20 cm × 27 cm) at
iso-center, this accuracy is sufficient to facilitate automatic positioning of
the X-ray system


FAU Authors / FAU Editors

Fahrig, Rebecca Prof. Dr.
Technische Fakultät
Maier, Andreas Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Zhong, Xia
Lehrstuhl für Informatik 5 (Mustererkennung)


External institutions
Siemens AG
Siemens Healthineers


How to cite

APA:
Zhong, X., Strobel, N., Birkhold, A., Kowarschik, M., Fahrig, R., & Maier, A. (2019). A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model. International Journal of Computer Assisted Radiology and Surgery, 14(1), 53--61. https://dx.doi.org/10.1007/s11548-018-1871-y

MLA:
Zhong, Xia, et al. "A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model." International Journal of Computer Assisted Radiology and Surgery 14.1 (2019): 53--61.

BibTeX: 

Last updated on 2019-11-04 at 17:08