Segmentation of subcutaneous fat within mouse skin in 3D OCT image data using random forests

Timo Kepp, Christine Droigk, Malte Casper, Michael Evers, Nunciada Salma, Dieter Manstein, Heinz Handels


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.
Original languageEnglish
Title of host publicationMedical Imaging 2018: Image Processing
EditorsElsa D. Angelini, Bennett A. Landmann
Number of pages8
Publication date2018
Pages1057426-1 - 1057426-8
ISBN (Print)9781510616370
Publication statusPublished - 2018
EventSPIE Medical Imaging 2018
- Marriott Marquis Houston, Houston, United States
Duration: 10.02.201815.02.2018


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