PET-Train: Automatic Ground Truth Generation from PET Acquisitions for Urinary Bladder Segmentation in CT Images using Deep Learning

Christina Gsaxner, Birgit Pfarrkirchner, Lydia Lindner, Antonio Pepe, Peter M. Roth, Jürgen Wallner, Jan Egger

Publikation: KonferenzbeitragPaper

Abstract

In this contribution, we propose an automatic ground truth generation approach that utilizes Positron Emission Tomography (PET) acquisitions to train neural networks for automatic urinary bladder segmentation in Computed Tomography (CT) images. We evaluated different deep learning architectures to segment the urinary bladder. However, deep neural networks require a large amount of training data, which is currently the main bottleneck in the medical field, because ground truth labels have to be created by medical experts on a time-consuming slice-by-slice basis. To overcome this problem, we generate the training data set from the PET data of combined PET/CT acquisitions. This can be achieved by applying simple thresholding to the PET data, where the radiotracer accumulates very distinct in the urinary bladder. However, the ultimate goal is to entirely skip PET imaging and its additional radiation exposure in the future, and only use CT images for segmentation.

Originalspracheenglisch
DOIs
PublikationsstatusVeröffentlicht - 2019
Veranstaltung2018 IEEE Biomedical Engineering International Conference - Chiang Mai, Thailand
Dauer: 21 Nov 2018 → …
Konferenznummer: 11

Konferenz

Konferenz2018 IEEE Biomedical Engineering International Conference
KurztitelBMEiCON 2018
LandThailand
OrtChiang Mai
Zeitraum21/11/18 → …

ASJC Scopus subject areas

  • Artificial intelligence
  • !!Instrumentation
  • !!Biomedical Engineering

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