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Proceedings Paper

IRIS: interactive real-time feedback image segmentation with deep learning
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Paper Abstract

Volumetric examinations of the aorta are nowadays of crucial importance for the management of critical pathologies such as aortic dissection, aortic aneurism, and other pathologies, which affect the morphology of the artery. These examinations usually begin with the acquisition of a Computed Tomography Angiography (CTA) scan from the patient, which is later on postprocessed to reconstruct the 3D geometry of the aorta. The first postprocessing step is referred to as segmentation. Different algorithms have been suggested for the segmentation of the aorta; including interactive methods, as well as fully automatic methods. Interactive methods need to be fine-tuned on each single CTA scan and result in longer duration of the process, whereas fully automatic methods require the possession of a large amount of labeled training data. In this work, we introduce a hybrid approach by combining a deep learning method with a consolidated interaction technique. In particular, we trained a 2D and a 3D U-Net on a limited number of patches extracted from 25 labeled CTA scans. Afterwards, we use an interactive approach, which consists in defining a region of interest (ROI) by just placing a seed point. This seed point is later used as the center of a 2D or 3D patch to be fed to the 2D or 3D U-Net, respectively. Due to the low content variation of these patches, this method allows to correctly segment the ROIs without the need for parameter tuning for each dataset and with a smaller training dataset, requiring the same minimal interaction as state-of-the-art interactive methods. Later on, the new segmented CTA scans can be further used to train a convolutional network for a fully automatic approach.

Paper Details

Date Published: 28 February 2020
PDF: 6 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113170R (28 February 2020); doi: 10.1117/12.2551354
Show Author Affiliations
Antonio Pepe, Stanford Univ. School of Medicine (United States)
Technische Univ. Graz (Austria)
Computer Algorithms for Medicine Lab. (Austria)
Richard Schussnig, Technische Univ. Graz (Austria)
Jianning Li, Technische Univ. Graz (Austria)
Computer Algorithms for Medicine Lab. (Austria)
Christina Gsaxner, Technische Univ. Graz (Austria)
Computer Algorithms for Medicine Lab. (Austria)
Medizinischen Univ. Graz (Austria)
Xiaojun Chen, Shanghai Jiao Tong Univ. (China)
Thomas-Peter Fries, Technische Univ. Graz (Austria)
Jan Egger, Technische Univ. Graz (Austria)
Computer Algorithms for Medicine Lab. (Austria)
Medizinischen Univ. Graz (Austria)


Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

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