  Automated medical image segmentation plays an important role in many clinical
applications, which however is a very challenging task, due to complex
background texture, lack of clear boundary and significant shape and texture
variation between images. Many researchers proposed an encoder-decoder
architecture with skip connections to combine low-level feature maps from the
encoder path with high-level feature maps from the decoder path for
automatically segmenting medical images. The skip connections have been shown
to be effective in recovering fine-grained details of the target objects and
may facilitate the gradient back-propagation. However, not all the feature maps
transmitted by those connections contribute positively to the network
performance. In this paper, to adaptively select useful information to pass
through those skip connections, we propose a novel 3D network with
self-supervised function, named selective information passing network
(SIP-Net). We evaluate our proposed model on the MICCAI Prostate MR Image
Segmentation 2012 Grant Challenge dataset, TCIA Pancreas CT-82 and MICCAI 2017
Liver Tumor Segmentation (LiTS) Challenge dataset. The experimental results
across these data sets show that our model achieved improved segmentation
results and outperformed other state-of-the-art methods. The source code of
this work is available at https://github.com/ahukui/SIPNet.
