Skip to main content
main-content

13.10.2018 | Original Article

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

Zeitschrift:
International Journal of Computer Assisted Radiology and Surgery
Autoren:
Xia Zhong, Norbert Strobel, Annette Birkhold, Markus Kowarschik, Rebecca Fahrig, Andreas Maier

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 \(12.19 \pm 6.98\ \hbox {mm}\).

Conclusion

Considering that interventional X-ray imaging systems can have detectors covering an area of about \(200\ \hbox {mm} \times 266\ \hbox {mm}\) (\(20\ \hbox {cm} \times 27\ \hbox {cm}\)) at iso-center, this accuracy is sufficient to facilitate automatic positioning of the X-ray system.

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

★ PREMIUM-INHALT
e.Med Interdisziplinär

Mit e.Med Interdisziplinär erhalten Sie Zugang zu allen CME-Fortbildungen und Fachzeitschriften auf SpringerMedizin.de. Zusätzlich können Sie eine Zeitschrift Ihrer Wahl in gedruckter Form beziehen – ohne Aufpreis.

Jetzt bestellen und 50 € OTTO-Gutschein sichern!

Weitere Produktempfehlungen anzeigen
Literatur
Über diesen Artikel
  1. Das kostenlose Testabonnement läuft nach 14 Tagen automatisch und formlos aus. Dieses Abonnement kann nur einmal getestet werden.

  2. Das kostenlose Testabonnement läuft nach 14 Tagen automatisch und formlos aus. Dieses Abonnement kann nur einmal getestet werden.

Neu im Fachgebiet Radiologie


 

Meistgelesene Bücher aus der Radiologie

2016 | Buch

Medizinische Fremdkörper in der Bildgebung

Thorax, Abdomen, Gefäße und Kinder

Dieses einzigartige Buch enthält ca. 1.600 hochwertige radiologische Abbildungen und Fotos iatrogen eingebrachter Fremdmaterialien im Röntgenbild und CT.

Herausgeber:
Dr. med. Daniela Kildal

2011 | Buch

Atlas Klinische Neuroradiologie des Gehirns

Radiologie lebt von Bildern! Der vorliegende Atlas trägt dieser Tatsache Rechnung. Sie finden zu jedem Krankheitsbild des Gehirns Referenzbilder zum Abgleichen mit eigenen Befunden.

Autoren:
Priv.-Doz. Dr. med. Jennifer Linn, Prof. Dr. med. Martin Wiesmann, Prof. Dr. med. Hartmut Brückmann

Mail Icon II Newsletter

Bestellen Sie unseren kostenlosen Newsletter Update Radiologie und bleiben Sie gut informiert – ganz bequem per eMail.

Bildnachweise