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Abstract #5662

Deep Convolutional Auto-Encoder and 3D Deformable Approach for Tissue Segmentation in Magnetic Resonance Imaging

Fang Liu1, Zhaoye Zhou2, Hyungseok Jang1, Alan McMillan1, and Richard Kijowski1

1Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, 2Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States

A fully-automated segmentation pipeline was built by combining a deep Convolutional Auto-Encoder (CAE) network and 3D simplex deformable modeling. The CAE was applied as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification. The 3D simplex deformable modeling refined output from CAE to preserve the overall shape and maintain a desirable smooth surface for structure. The fully-automated segmentation method was tested using a publicly available knee joint image dataset to compare with currently used state-of-the-art segmentation methods. The fully-automated method was also evaluated on morphological MR images with different tissue contrasts and image training datasets.

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