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Abstract #3502

Pelvic Organ Segmentation with Sample Attention based Stochastic Connection Networks

Dong Nie1,2, Li Wang2, Jun Lian3, and Dinggang Shen2

1Department of Computer Science, UNC-Chapel Hill, Chapel Hill, NC, United States, 2Department of Radiology and BRIC, UNC-Chapel Hill, Chapel Hill, NC, United States, 3Department of Radiation Oncology, UNC-Chapel Hill, Chapel Hill, NC, United States

Accurate segmentation of pelvic organs is important for prostate radiation therapy. Modern radiation therapy starts to use magnetic resonance image (MRI) as an alternative to CT image, because of the superior soft tissue contrast of MRI and also no risk of radiation exposure. In this abstract, we propose a novel deep network architecture, called “Sample Attention based Stochastic Connection Networks” (SASCNet), to delineate pelvic organs from MRI in an end-to-end fashion. Our proposed network has two main contributions: 1) We propose a novel randomized connection module and adopt it as a basic unit to combine the shallower and deeper layers in the fully convolutional networks (FCN); 2) We propose a novel adversarial attention mechanism to automatically dispatch sample importance so that we can avoid the domination of easy samples in training the network. Experimental results show that our SASCNet achieves competitive segmentation accuracy.

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