Fully-automatic CT data preparation for interventional X-ray skin dose simulation

Roser P, Birkhold A, Preuhs A, Stimpel B, Syben C, Strobel N, Kowarschik M, Fahrig R, Maier A (2020)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Pages Range: 1-6

Conference Proceedings Title: Informatik Aktuell Bildverarbeitung für die Medizin 2020 Algorithmen - Systeme - Anwendungen

Event location: Berlin

DOI: 10.1007/978-3-658-29267-6_26

Abstract

Recently, deep learning (DL) found its way to interventional X-ray skin dose estimation. While its performance was found to be acceptable, even more accurate results could be achieved if more data sets were available for training. One possibility is to turn to computed tomography (CT) data sets. Typically, computed tomography (CT) scans can be mapped to tissue labels and mass densities to obtain training data. However, care has to be taken to make sure that the different clinical settings are properly accounted for. First, the interventional environment is characterized by wide variety of table setups that are significantly different from the typical patient tables used in conventional CT. This cannot be ignored, since tables play a crucial role in sound skin dose estimation in an interventional setup, e.,g., when the X-ray source is directly underneath a patient (posterior-anterior view). Second, due to interpolation errors, most CT scans do not facilitate a clean segmentation of the skin border. As a solution to these problems, we applied connected component labeling (CCL) and Canny edge detection to (a) robustly separate the patient from the table and (b) to identify the outermost skin layer. Our results show that these extensions enable fully-automatic, generalized pre-processing of CT scans for further simulation of both skin dose and corresponding X-ray projections.

Authors with CRIS profile

Involved external institutions

Siemens AG Germany (DE) Hochschule für angewandte Wissenschaften Würzburg-Schweinfurt Germany (DE)

How to cite

APA:

Roser, P., Birkhold, A., Preuhs, A., Stimpel, B., Syben, C., Strobel, N.,... Maier, A. (2020). Fully-automatic CT data preparation for interventional X-ray skin dose simulation. In Informatik Aktuell Bildverarbeitung für die Medizin 2020 Algorithmen - Systeme - Anwendungen (pp. 1-6). Berlin, DE.

MLA:

Roser, Philipp, et al. "Fully-automatic CT data preparation for interventional X-ray skin dose simulation." Proceedings of the Bildverarbeitung für die Medizin: Algorithmen - Systeme - Anwendungen, Berlin 2020. 1-6.

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