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Proceedings Paper

Segmentation of subcutaneous fat within mouse skin in 3D OCT image data using random forests
Author(s): Timo Kepp; Christine Droigk; Malte Casper; Michael Evers; Nunciada Salma; Dieter Manstein M.D.; Heinz Handels
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Paper Abstract

Cryolipolysis is a well-established cosmetic procedure for non-invasive local fat reduction. This technique selectively destroys subcutaneous fat cells using controlled cooling. Thickness measurements of subcutaneous fat were conducted using a mouse model. For detailed examination of mouse skin optical coherence tomography (OCT) was performed, which is a non-invasive imaging modality. Due to a high number of image slices manual delineation is not feasible. Therefore, automatic segmentation algorithms are required. In this work an algorithm for the automatic 3D segmentation of the subcutaneous fat layer is presented, which is based on a random forest classification followed by a graph-based refinement step. Our approach is able to accurately segment the subcutaneous fat layer with an overall average symmetric surface distance of 11.80±6.05 μm and Dice coefficient of 0.921 ± 0.045. Furthermore, it was shown that the graph-based refining step leads to increased accuracy and robustness of the segmentation results of the random forest classifier.

Paper Details

Date Published: 2 March 2018
PDF: 8 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057426 (2 March 2018); doi: 10.1117/12.2290085
Show Author Affiliations
Timo Kepp, Univ. zu Lübeck (Germany)
Christine Droigk, Univ. zu Lübeck (Germany)
Malte Casper, Univ. zu Lübeck (Germany)
Massachusetts General Hospital (United States)
Michael Evers, Univ. zu Lübeck (Germany)
Massachusetts General Hospital (United States)
Nunciada Salma, Massachusetts General Hospital (United States)
Dieter Manstein M.D., Massachusetts General Hospital (United States)
Heinz Handels, Univ. zu Lübeck (Germany)

Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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