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Proceedings Paper

Active appearance model and deep learning for more accurate prostate segmentation on MRI
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Paper Abstract

Prostate segmentation on 3D MR images is a challenging task due to image artifacts, large inter-patient prostate shape and texture variability, and lack of a clear prostate boundary specifically at apex and base levels. We propose a supervised machine learning model that combines atlas based Active Appearance Model (AAM) with a Deep Learning model to segment the prostate on MR images. The performance of the segmentation method is evaluated on 20 unseen MR image datasets. The proposed method combining AAM and Deep Learning achieves a mean Dice Similarity Coefficient (DSC) of 0.925 for whole 3D MR images of the prostate using axial cross-sections. The proposed model utilizes the adaptive atlas-based AAM model and Deep Learning to achieve significant segmentation accuracy.

Paper Details

Date Published: 21 March 2016
PDF: 9 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842I (21 March 2016); doi: 10.1117/12.2216286
Show Author Affiliations
Ruida Cheng, National Institutes of Health (United States)
Holger R. Roth, National Institutes of Health (United States)
Le Lu, National Institutes of Health (United States)
Shijun Wang, National Institutes of Health (United States)
Baris Turkbey, National Institutes of Health (United States)
William Gandler, National Institutes of Health (United States)
Evan S. McCreedy, National Institutes of Health (United States)
Harsh K. Agarwal, National Institutes of Health (United States)
Peter Choyke, National Institutes of Health (United States)
Ronald M. Summers, National Institutes of Health (United States)
Matthew J. McAuliffe, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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