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Abstract #1587

Segmentation of CMF Bones from MRI with A Cascade Deep Learning Framework

Dong Nie1, Li Wang1, Jianfu Li2, Daeseung Kim2, James J. Xia2, and Dinggang Shen1

1Department of Radiology and BRIC, UNC-Chapel Hill, USA, Chapel Hill, NC, United States, 2Houston Methodist Hospital, Houston, TX, USA

Accurate segmentation of CMF bones from MRI is one of the most important fundamental steps in clinical applications, and it can also be used in other areas, such as character animation and assistive robotics. In this paper, we propose a cascade framework based on the recently well-received and prominent deep learning methods. Specifically, we first propose a 3D fully convolutional network architecture for a coarse segmentation of the bone tissue. Further, we propose to utilize CNN for fine-grained level segmentation around the predicted bone tissue area. The conducted experiments show that our proposed 3D deep learning model could achieve good performance in terms of segmentation accuracy.

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