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An automatically generated texture-based atlas of the lungs
Author(s): Yashin Dicente Cid; Oula Puonti; Alexandra Platon; Koen Van Leemput; Henning Müller; Pierre-Alexandre Poletti
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Paper Abstract

Many pulmonary diseases can be characterized by visual abnormalities on lung CT scans. Some diseases manifest similar defects but require completely different treatments, as is the case for Pulmonary Hypertension (PH) and Pulmonary Embolism (PE): both present hypo- and hyper-perfused regions but with different distribution across the lung and require different treatment protocols. Finding these distributions by visual inspection is not trivial even for trained radiologists who currently use invasive catheterism to diagnose PH. A Computer-Aided Diagnosis (CAD) tool that could facilitate the non-invasive diagnosis of these diseases can benefit both the radiologists and the patients. Most of the visual differences in the parenchyma can be characterized using texture descriptors. Current CAD systems often use texture information but the texture is either computed in a patch-based fashion, or based on an anatomical division of the lung. The difficulty of precisely finding these divisions in abnormal lungs calls for new tools for obtaining new meaningful divisions of the lungs. In this paper we present a method for unsupervised segmentation of lung CT scans into subregions that are similar in terms of texture and spatial proximity. To this extent, we combine a previously validated Riesz-wavelet texture descriptor with a well-known superpixel segmentation approach that we extend to 3D. We demonstrate the feasibility and accuracy of our approach on a simulated texture dataset, and show preliminary results for CT scans of the lung comparing subjects suffering either from PH or PE. The resulting texture-based atlas of individual lungs can potentially help physicians in diagnosis or be used for studying common texture distributions related to other diseases.

Paper Details

Date Published: 27 February 2018
PDF: 8 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753A (27 February 2018); doi: 10.1117/12.2294004
Show Author Affiliations
Yashin Dicente Cid, Univ. of Applied Sciences Western Switzerland (Switzerland)
Univ. of Geneva (Switzerland)
Oula Puonti, Technical Univ. of Denmark (Denmark)
Copenhagen Univ. Hospital Hvidovre (Denmark)
Alexandra Platon, Geneva Univ. Hospitals (Switzerland)
Koen Van Leemput, Technical Univ. of Denmark (Denmark)
Massachusetts General Hospital, Harvard Medical School (United States)
Henning Müller, Univ. of Applied Sciences Western Switzerland (Switzerland)
Univ. of Geneva (Switzerland)
Pierre-Alexandre Poletti, Geneva Univ. Hospitals (Switzerland)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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