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Registration based detection and quantification of intracranial aneurysm growth
Author(s): Žiga Bizjak; Tim Jerman; Boštjan Likar; Franjo Pernuš; Aichi Chien; Žiga Špiclin
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Paper Abstract

As growing aneurysms are very likely to rupture, features to detect and quantify the growth are needed in order to assess rupture risk. So far cross-sectional features like maximum dome size were used, however, independent analysis of baseline and follow-up aneurysm shapes may bias these features and thereby conceal the often subtle changes of aneurysm morphology. We propose to detect and quantify aneurysm growth using shape coregistration, composed of globally optimal rigid registration, followed by non-rigid warping of baseline mesh to the follow-up mesh. Aneurysm isolation algorithm is used to constrain the registration to parent vessels and to aneurysm dome in the rigid and non-rigid registration steps, respectively. Based on the analysis of the obtained deformation field, two novel morphologic features were proposed, namely the relative differential surface area and median path length, normalized by maximum dome size. The morphological features were extracted and studied on a CTA image dataset of 20 patients, each containing one unruptured intracranial saccular aneurysm (maximal dome diameters were from 1.4 to 12.2 mm). For a baseline performance comparison, five cross-sectional features were also extracted and their relative change computed. The two novel registration based features performed best as demonstrated by lowest p-values (<0.003) obtained by Mann-Whitney U-test and highest area under the curve (>0.89) obtained from a ROC analysis. The proposed differential features are inherently longitudinal, taking into consideration baseline and follow-up aneurysm shape information at once, and seem to enable an interventional neuroradiologist to differentiate better between low- and high-rupture-risk aneurysms.

Paper Details

Date Published: 13 March 2019
PDF: 11 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095007 (13 March 2019); doi: 10.1117/12.2512781
Show Author Affiliations
Žiga Bizjak, Univ. of Ljubljana (Slovenia)
Tim Jerman, Univ. of Ljubljana (Slovenia)
Boštjan Likar, Univ. of Ljubljana (Slovenia)
Franjo Pernuš, Univ. of Ljubljana (Slovenia)
Aichi Chien, David Geffen School of Medicine, Univ. of California, Los Angeles (United States)
Žiga Špiclin, Univ. of Ljubljana (Slovenia)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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