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Visual comparison of 3D medical image Segmentation Algorithms Based on Statistical Shape Models

 
: Geurts, Alexander; Sakas, Georgios; Kuijper, Arjan; Becker, Meike; Landesberger, Tatiana von

:

Duffy, Vincent G. (Ed.):
Digital human modeling. Applications in health, safety, ergonomics and risk management. Ergonomics and health. 6th international conference, DHM 2015. Pt.2 : Held as part of HCI International 2015, Los Angeles, CA, USA, August 2 - 7, 2015; Proceedings
Cham: Springer International Publishing, 2015 (Lecture Notes in Computer Science 9185)
ISBN: 978-3-319-21069-8 (Print)
ISBN: 978-3-319-21070-4 (Online)
S.336-344
International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management (DHM) <6, 2015, Los Angeles/Calif.>
International Conference on Human-Computer Interaction (HCI International) <17, 2015, Los Angeles/Calif.>
Englisch
Konferenzbeitrag
Fraunhofer IGD ()
Business Field: Visual decision support; Research Area: Computer vision (CV); Research Area: Human computer interaction (HCI); Research Area: Modeling (MOD); Forschungsgruppe Medical Computing (MECO); Forschungsgruppe Visual Search and Analysis (VISA); visual analytic; image segmentation; evaluation of segmentation

Abstract
3D medical image segmentation is needed for diagnosis and treatment. As manual segmentation is very costly, automatic segmentation algorithms are needed. For finding best algorithms, several algorithms need to be evaluated on a set of organ instances. This is currently difficult due to dataset size and complexity. In this paper, we present a novel method for comparison and evaluation of several algorithms that automatically segment 3D medical images. It combines algorithmic data analysis with interactive data visualization. A clustering algorithm identifies regions of common quality across the segmented data set for each algorithm. The comparison identifies best algorithms per region. Interactive views show the algorithm quality. We applied our approach to a real-world cochlea dataset, which was segmented with several algorithms. Our approach allowed segmentation experts to compare algorithms on regional level and to identify best algorithms per region.

: http://publica.fraunhofer.de/dokumente/N-515750.html