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

Ruida Cheng; Holger R. Roth; Le Lu; Shijun Wang; Baris Turkbey; William Gandler; Evan S. McCreedy; Harsh K. Agarwal; Peter Choyke; Ronald M. Summers; Matthew J. McAuliffe

doi:10.1117/12.2216286

21 March 2016 Active appearance model and deep learning for more accurate prostate segmentation on MRI

Ruida Cheng, Holger R. Roth, Le Lu, Shijun Wang, Baris Turkbey, William Gandler, Evan S. McCreedy, Harsh K. Agarwal, Peter Choyke, Ronald M. Summers, Matthew J. McAuliffe

Author Affiliations +

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97842I (2016) https://doi.org/10.1117/12.2216286
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

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Ruida Cheng, Holger R. Roth, Le Lu, Shijun Wang, Baris Turkbey, William Gandler, Evan S. McCreedy, Harsh K. Agarwal, Peter Choyke, Ronald M. Summers, and Matthew J. McAuliffe "Active appearance model and deep learning for more accurate prostate segmentation on MRI", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842I (21 March 2016); https://doi.org/10.1117/12.2216286

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